With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Network traffic classification is a crucial research area aimed at improving quality of service,simplifying network management,and enhancing network security.To address the growing complexity of cryptography,researche...Network traffic classification is a crucial research area aimed at improving quality of service,simplifying network management,and enhancing network security.To address the growing complexity of cryptography,researchers have proposed various machine learning and deep learning approaches to tackle this challenge.However,existing mainstream methods face several general issues.On one hand,the widely used Transformer architecture exhibits high computational complexity,which negatively impacts its efficiency.On the other hand,traditional methods are often unreliable in traffic representation,frequently losing important byte information while retaining unnecessary biases.To address these problems,this paper introduces the Swin Transformer architecture into the domain of network traffic classification and proposes the NetST(Network Swin Transformer)model.This model improves the Swin Transformer to better accommodate the characteristics of network traffic,effectively addressing efficiency issues.Furthermore,this paper presents a traffic representation scheme designed to extract meaningful information from large volumes of traffic while minimizing bias.We integrate four datasets relevant to network traffic classification for our experiments,and the results demonstrate that NetST achieves a high accuracy rate while maintaining low memory usage.展开更多
In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly d...In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific nodes.This study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node information.The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet metadata.This makes it well-suited for direct application in IoT environments due to its low resource consumption.In experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based features.Additionally,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM.Furthermore,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling methods.This study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.展开更多
Accurate classification of encrypted traffic plays an important role in network management.However,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability ...Accurate classification of encrypted traffic plays an important role in network management.However,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability to classify traffic with multi-level features,and degradation due to limited training traffic size.To address these problems,this paper proposes a traffic granularity-based cryptographic traffic classification method,called Granular Classifier(GC).In this paper,a novel Cardinality-based Constrained Fuzzy C-Means(CCFCM)clustering algorithm is proposed to address the problem caused by limited training traffic,considering the ratio of cardinality that must be linked between flows to achieve good traffic partitioning.Then,an original representation format of traffic is presented based on granular computing,named Traffic Granules(TG),to accurately describe traffic structure by catching the dispersion of different traffic features.Each granule is a compact set of similar data with a refined boundary by excluding outliers.Based on TG,GC is constructed to perform traffic classification based on multi-level features.The performance of the GC is evaluated based on real-world encrypted network traffic data.Experimental results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.展开更多
Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traff...Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.展开更多
Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Altho...Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.展开更多
The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identific...The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.展开更多
Traffic identification becomes more important,yet more challenging as related encryption techniques are rapidly developing nowadays.Unlike recent deep learning methods that apply image processing to solve such encrypt...Traffic identification becomes more important,yet more challenging as related encryption techniques are rapidly developing nowadays.Unlike recent deep learning methods that apply image processing to solve such encrypted traffic problems,in this pa⁃per,we propose a method named Payload Encoding Representation from Transformer(PERT)to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique.By implementing traffic classification experiments on a pub⁃lic encrypted traffic data set and our captured Android HTTPS traffic,we prove the pro⁃posed method can achieve an obvious better effectiveness than other compared baselines.To the best of our knowledge,this is the first time the encrypted traffic classification with the dynamic word embedding has been addressed.展开更多
Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security.Machine learning and deep learning have become mainstream methods to analyze traffic,and labeled da...Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security.Machine learning and deep learning have become mainstream methods to analyze traffic,and labeled dataset construction is the basis.Android occupies a huge share of the mobile operating system market.Instant Messaging(IM)applications are important tools for people communication.But such applications have complex functions which frequently switched,so it is difficult to obtain function-level labels.The existing function-level public datasets in Android are rare and noisy,leading to research stagnation.Most labeled samples are collected with WLAN devices,which cannot exclude the operating system background traffic.At the same time,other datasets need to obtain root permission or use scripts to simulate user behavior.These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained.Previous work(Chen et al.in Appl Sci 12(22):11731,2022)proposed a one-stop automated encrypted traffic labeled sample collection,construction,and correlation system,A3C,running at the application-level in Android.This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android,F3L.The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground.It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions.The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels.Experiments show that the method can match the correct label within 3 s after the user operation.展开更多
The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable chall...The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the TB-Graph model, an encrypted malicious traffic classification model based on a relational graph attention network. The model is a heterogeneous traffic burst graph that embeds side-channel features, which are unaffected by encryption, into the graph nodes and connects them with three different types of burst edges. Subsequently, we design a relational positional coding that prevents the loss of temporal relationships between the original traffic flows during graph transformation. Ultimately, TB-Graph leverages the powerful graph representation learning capabilities of Relational Graph Attention Network (RGAT) to extract latent behavioral features from the burst graph nodes and edge relationships. Experimental results show that TB-Graph outperforms various state-of-the-art methods in fine-grained encrypted malicious traffic classification tasks on two public datasets, indicating its enhanced capability for identifying encrypted malicious traffic.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lackof performance of each component, such as computing resources like CPUs and batteries, to encrypt and d...In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lackof performance of each component, such as computing resources like CPUs and batteries, to encrypt and decryptdata. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomicalfinancial and human casualties. For this reason, the application of encrypted communication to IoT has beenrequired, and the application of encrypted communication to IoT has become possible due to improvements inthe computing performance of IoT devices and the development of lightweight cryptography. The applicationof encrypted communication in IoT has made it possible to use encrypted communication channels to launchcyberattacks. The approach of extracting evidence of an attack based on the primary information of a networkpacket is no longer valid because critical information, such as the payload in a network packet, is encrypted byencrypted communication. For this reason, technology that can detect cyberattacks over encrypted network trafficoccurring in IoT environments is required. Therefore, this research proposes an encrypted cyberattack detectionsystem for the IoT (ECDS-IoT) that derives valid features for cyberattack detection from the cryptographic networktraffic generated in the IoT environment and performs cyberattack detection based on the derived features. ECDS-IoT identifies identifiable information from encrypted traffic collected in IoT environments and extracts statistics-based features through statistical analysis of identifiable information. ECDS-IoT understands information aboutnormal data by learning only statistical features extracted from normal data. ECDS-IoT detects cyberattacks basedonly on the normal data information it has trained. To evaluate the cyberattack detection performance of theproposed ECDS-IoT in this research, ECDS-IoT used CICIoT2023, a dataset containing encrypted traffic generatedby normal and seven categories of cyberattacks in the IoT environment and experimented with cyberattackdetection on encrypted traffic using Autoencoder, RNN, GRU, LSTM, BiLSTM, and AE-LSTM algorithms. Asa result of evaluating the performance of cyberattack detection for encrypted traffic, ECDS-IoT achieved highperformance such as accuracy 0.99739, precision 0.99154, recall 1.0, F1 score 0.99575, and ROC_AUC 0.99822when using the AE-LSTM algorithm. As shown by the cyberattack detection results of ECDS-IoT, it is possibleto detect most cyberattacks through encrypted traffic. By applying ECDS-IoT to IoT, it can effectively detectcyberattacks concealed in encrypted traffic, promoting the efficient operation of IoT and preventing financial andhuman damage caused by cyberattacks.展开更多
Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,...Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.展开更多
While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning me...While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.展开更多
As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditio...As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.展开更多
In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic...In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic. It can filter out a large amount of non-malicious traffic, greatly in, roving the detection efficiency. A Sequential Probability Ratio Test （SPRT）-based method can find spatialtemporal correlations in suspicious botnet traffic and make an accurate judgment. Experimental resuks show that the false positive and false nega- tive rates can be controlled within a certain range.展开更多
Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out se...Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.展开更多
Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network(CNN),recurrent neural network(RNN),and stacked autoencoder(SAE...Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network(CNN),recurrent neural network(RNN),and stacked autoencoder(SAE),and only construct a shallow network to extract features,which leads to the low accuracy of encrypted traffic classification,an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed.Bottleneck transformer network(BoTNet)was used to extract spatial features and bi-directional long short-term memory(BiLSTM)was used to extract temporal features.After the two sub-networks are parallelized,the feature fusion method of early fusion was used in the framework to perform feature fusion.Finally,the encrypted traffic was identified through the fused features.The experimental results show that the BiLSTM and BoTNet fusion transformer(BTFT)model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features.The accuracy rate of a virtual private network(VPN)and non-VPN binary classification is 99.9%,and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%.展开更多
VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and c...VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.展开更多
With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged...With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.展开更多
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金supported by National Natural Science Foundation of China(62473341)Key Technologies R&D Program of Henan Province(242102211071,252102211086,252102210166).
文摘Network traffic classification is a crucial research area aimed at improving quality of service,simplifying network management,and enhancing network security.To address the growing complexity of cryptography,researchers have proposed various machine learning and deep learning approaches to tackle this challenge.However,existing mainstream methods face several general issues.On one hand,the widely used Transformer architecture exhibits high computational complexity,which negatively impacts its efficiency.On the other hand,traditional methods are often unreliable in traffic representation,frequently losing important byte information while retaining unnecessary biases.To address these problems,this paper introduces the Swin Transformer architecture into the domain of network traffic classification and proposes the NetST(Network Swin Transformer)model.This model improves the Swin Transformer to better accommodate the characteristics of network traffic,effectively addressing efficiency issues.Furthermore,this paper presents a traffic representation scheme designed to extract meaningful information from large volumes of traffic while minimizing bias.We integrate four datasets relevant to network traffic classification for our experiments,and the results demonstrate that NetST achieves a high accuracy rate while maintaining low memory usage.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509,Development of Security Monitoring Technology Based Network Behavior against Encrypted Cyber Threats in ICT Convergence Environment).
文摘In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific nodes.This study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node information.The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet metadata.This makes it well-suited for direct application in IoT environments due to its low resource consumption.In experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based features.Additionally,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM.Furthermore,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling methods.This study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.
基金supported in part by the Shandong Provincial Natural Science Foundation under Grant ZR2021QF008the National Natural Science Foundation of China under Grant 62072351+1 种基金in part by the open research project of ZheJiang Lab under grant 2021PD0AB01in part by the 111 Project under Grant B16037。
文摘Accurate classification of encrypted traffic plays an important role in network management.However,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability to classify traffic with multi-level features,and degradation due to limited training traffic size.To address these problems,this paper proposes a traffic granularity-based cryptographic traffic classification method,called Granular Classifier(GC).In this paper,a novel Cardinality-based Constrained Fuzzy C-Means(CCFCM)clustering algorithm is proposed to address the problem caused by limited training traffic,considering the ratio of cardinality that must be linked between flows to achieve good traffic partitioning.Then,an original representation format of traffic is presented based on granular computing,named Traffic Granules(TG),to accurately describe traffic structure by catching the dispersion of different traffic features.Each granule is a compact set of similar data with a refined boundary by excluding outliers.Based on TG,GC is constructed to perform traffic classification based on multi-level features.The performance of the GC is evaluated based on real-world encrypted network traffic data.Experimental results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB4500800)the National Science Foundation of China(No.42071431).
文摘Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.
基金supported by the National Natural Science Foundation of China (Grants Nos.61931004,62072250)the Talent Launch Fund of Nanjing University of Information Science and Technology (2020r061).
文摘Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.
基金This work is supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.under Grant No.J2020068.
文摘The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.
文摘Traffic identification becomes more important,yet more challenging as related encryption techniques are rapidly developing nowadays.Unlike recent deep learning methods that apply image processing to solve such encrypted traffic problems,in this pa⁃per,we propose a method named Payload Encoding Representation from Transformer(PERT)to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique.By implementing traffic classification experiments on a pub⁃lic encrypted traffic data set and our captured Android HTTPS traffic,we prove the pro⁃posed method can achieve an obvious better effectiveness than other compared baselines.To the best of our knowledge,this is the first time the encrypted traffic classification with the dynamic word embedding has been addressed.
基金supported by the General Program of the National Natural Science Foundation of China under Grant No.62172093.
文摘Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security.Machine learning and deep learning have become mainstream methods to analyze traffic,and labeled dataset construction is the basis.Android occupies a huge share of the mobile operating system market.Instant Messaging(IM)applications are important tools for people communication.But such applications have complex functions which frequently switched,so it is difficult to obtain function-level labels.The existing function-level public datasets in Android are rare and noisy,leading to research stagnation.Most labeled samples are collected with WLAN devices,which cannot exclude the operating system background traffic.At the same time,other datasets need to obtain root permission or use scripts to simulate user behavior.These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained.Previous work(Chen et al.in Appl Sci 12(22):11731,2022)proposed a one-stop automated encrypted traffic labeled sample collection,construction,and correlation system,A3C,running at the application-level in Android.This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android,F3L.The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground.It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions.The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels.Experiments show that the method can match the correct label within 3 s after the user operation.
文摘The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the TB-Graph model, an encrypted malicious traffic classification model based on a relational graph attention network. The model is a heterogeneous traffic burst graph that embeds side-channel features, which are unaffected by encryption, into the graph nodes and connects them with three different types of burst edges. Subsequently, we design a relational positional coding that prevents the loss of temporal relationships between the original traffic flows during graph transformation. Ultimately, TB-Graph leverages the powerful graph representation learning capabilities of Relational Graph Attention Network (RGAT) to extract latent behavioral features from the burst graph nodes and edge relationships. Experimental results show that TB-Graph outperforms various state-of-the-art methods in fine-grained encrypted malicious traffic classification tasks on two public datasets, indicating its enhanced capability for identifying encrypted malicious traffic.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-00493,5G Massive Next Generation Cyber Attack Deception Technology Development).
文摘In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lackof performance of each component, such as computing resources like CPUs and batteries, to encrypt and decryptdata. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomicalfinancial and human casualties. For this reason, the application of encrypted communication to IoT has beenrequired, and the application of encrypted communication to IoT has become possible due to improvements inthe computing performance of IoT devices and the development of lightweight cryptography. The applicationof encrypted communication in IoT has made it possible to use encrypted communication channels to launchcyberattacks. The approach of extracting evidence of an attack based on the primary information of a networkpacket is no longer valid because critical information, such as the payload in a network packet, is encrypted byencrypted communication. For this reason, technology that can detect cyberattacks over encrypted network trafficoccurring in IoT environments is required. Therefore, this research proposes an encrypted cyberattack detectionsystem for the IoT (ECDS-IoT) that derives valid features for cyberattack detection from the cryptographic networktraffic generated in the IoT environment and performs cyberattack detection based on the derived features. ECDS-IoT identifies identifiable information from encrypted traffic collected in IoT environments and extracts statistics-based features through statistical analysis of identifiable information. ECDS-IoT understands information aboutnormal data by learning only statistical features extracted from normal data. ECDS-IoT detects cyberattacks basedonly on the normal data information it has trained. To evaluate the cyberattack detection performance of theproposed ECDS-IoT in this research, ECDS-IoT used CICIoT2023, a dataset containing encrypted traffic generatedby normal and seven categories of cyberattacks in the IoT environment and experimented with cyberattackdetection on encrypted traffic using Autoencoder, RNN, GRU, LSTM, BiLSTM, and AE-LSTM algorithms. Asa result of evaluating the performance of cyberattack detection for encrypted traffic, ECDS-IoT achieved highperformance such as accuracy 0.99739, precision 0.99154, recall 1.0, F1 score 0.99575, and ROC_AUC 0.99822when using the AE-LSTM algorithm. As shown by the cyberattack detection results of ECDS-IoT, it is possibleto detect most cyberattacks through encrypted traffic. By applying ECDS-IoT to IoT, it can effectively detectcyberattacks concealed in encrypted traffic, promoting the efficient operation of IoT and preventing financial andhuman damage caused by cyberattacks.
基金supported by National Key Research and Development Program of China(2022YFB3104903)S&T Program of Hebei(No.SZX2020034).
文摘Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.
基金This research was funded by National Natural Science Foundation of China under Grant No.61806171Sichuan University of Science&Engineering Talent Project under Grant No.2021RC15+2 种基金Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computer of Sichuan Province Universities on Bridge Inspection and Engineering under Grant No.2022QYJ06Sichuan University of Science&Engineering Graduate Student Innovation Fund under Grant No.Y2023115The Scientific Research and Innovation Team Program of Sichuan University of Science and Technology under Grant No.SUSE652A006.
文摘While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.
基金supported by the General Program of the National Natural Science Foundation of China under Grant No.62172093the National Key R&D Program of China under Grant No.2018YFB1800602+1 种基金2019 Industrial Internet Innovation and Development Project,Ministry of Industry and Information Technology(MIIT)under Grant No.6709010003Ministry of Education-China Mobile Research Fund under Grant No.MCM20180506。
文摘As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.
基金supported by the National Basic Research Program of China(973 Program)under Grant No.2011CB302903the Priority Academic Program Development of Jiangsu Higher Education Institutions under Grant No.YX002001
文摘In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic. It can filter out a large amount of non-malicious traffic, greatly in, roving the detection efficiency. A Sequential Probability Ratio Test （SPRT）-based method can find spatialtemporal correlations in suspicious botnet traffic and make an accurate judgment. Experimental resuks show that the false positive and false nega- tive rates can be controlled within a certain range.
基金supported by the People’s Public Security University of China central basic scientific research business program(No.2021JKF206).
文摘Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.
基金supported by the Science and Technology Project of the Headquarters of State Grid Corporation of China(5700-202152186A-0-0-00)。
文摘Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network(CNN),recurrent neural network(RNN),and stacked autoencoder(SAE),and only construct a shallow network to extract features,which leads to the low accuracy of encrypted traffic classification,an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed.Bottleneck transformer network(BoTNet)was used to extract spatial features and bi-directional long short-term memory(BiLSTM)was used to extract temporal features.After the two sub-networks are parallelized,the feature fusion method of early fusion was used in the framework to perform feature fusion.Finally,the encrypted traffic was identified through the fused features.The experimental results show that the BiLSTM and BoTNet fusion transformer(BTFT)model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features.The accuracy rate of a virtual private network(VPN)and non-VPN binary classification is 99.9%,and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%.
文摘VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.
基金the National Natural Science Foundation of China Youth Project(62302520).
文摘With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.
