The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulner...Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.展开更多
Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Althoug...Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
Co-residency of virtual machines(VMs) of different tenants on the same physical platform would possibly lead to cross-VM side-channel attacks in the cloud. While most of current countermeasures fail for real or immedi...Co-residency of virtual machines(VMs) of different tenants on the same physical platform would possibly lead to cross-VM side-channel attacks in the cloud. While most of current countermeasures fail for real or immediate deployment due to their requirement for modification of virtualization structure, we adopt dynamic migration, an inherent mechanism of the cloud platform, as a general defense against this kind of threats. To this end, we first set up a unified practical information leakage model which shows the factors affecting side channels and describes the way they influence the damage due to side-channel attacks. Since migration is adopted to limit the time duration of co-residency, we envision this defense as an optimization problem by setting up an Integer Linear Programming(ILP) to calculate optimal migration strategy, which is intractable due to high computational complexity. Therefore, we approximate the ILP with a baseline genetic algorithm, which is further improved for its optimality and scalability. Experimental results show that our migration-based defense can not only provide excellent security guarantees and affordable performance cost in both theoretical simulation and practical cloud environment, but also achieve better optimality and scalability than previous countermeasures.展开更多
This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control func...This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control functions of a cyber network and power flow characteristics of a power network,a power cyber-physical system model is established.Then,the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied,and a cascading failure analysis process is proposed for the cyber-attack environment.In addition,a vulnerability evaluation index is defined from two perspectives,i.e.,the topology integrity and power network operation characteristics.Moreover,the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyberphysical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index.Finally,an IEEE14-bus power network is selected for constructing a power cyber-physical system.Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks,which affect the ability of the cyber network to suppress the propagation of cascading failures,and expand the scale of the cascading failures.The vulnerability evaluation index is calculated for each node,so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.展开更多
In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method,this pape...In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method,this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical detection.For the spatial image,this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded domain.Then construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography,and use the minimum distortion coding to realize the embedding of the secret messages.Finally,according to the embedding modification amplitude of secret messages in the new embedded domain,the quantization index modulation algorithm is applied to realize the final embedding of secret messages in the original embedded domain.The experimental results show that the algorithm proposed is robust to the three common interpolation attacks including the nearest neighbor interpolation,the bilinear interpolation and the bicubic interpolation.And the average correct extraction rate of embedded messages increases from 50%to over 93% after 0.5 times-fold scaling attack using the bicubic interpolation method,compared with the classical steganography algorithm S-UNIWARD.Also the algorithm proposed has higher detection resistance than the original watermarking algorithm based on quantization index modulation.展开更多
Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronizati...Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.展开更多
The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning o...The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.展开更多
Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectiv...Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectively.But they are susceptible to malicious attacks,which mainly targets particular significant nodes.Therefore,the robustness of the network becomes important for ensuring the network security.This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization(RHAFS-SA)Algorithm.It is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree distribution.The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm(IAFSA)by the simulated annealing(SA)algorithm.The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate.For experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks.The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.展开更多
对于多通道软件无线电设备,解决设备内部网络与外部不安全网络间的多路并行数据高速传输问题是一项设计难点。基于此,提出一种基于软件无线电软件通信体系架构(Software Communication Architecture,SCA)硬件抽象层标准的多模式、高速...对于多通道软件无线电设备,解决设备内部网络与外部不安全网络间的多路并行数据高速传输问题是一项设计难点。基于此,提出一种基于软件无线电软件通信体系架构(Software Communication Architecture,SCA)硬件抽象层标准的多模式、高速率接口适配模块设计方法,通过多通道的虚拟化接口设计,实现多波形业务数据并行数据流的复接与分发。经过平台验证,本设计支持总吞吐量不低于12 Gb/s的多路并行业务数据传输,可满足多通道、多模式下软件无线电波形的并行数据复接与分发需求。展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resi...Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification.Some of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these features.Therefore,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions.The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks.The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its robustness.For this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this sense.Sensitivity,luminance,inter and intra-block contrast are used to adjust the JND values.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional m...This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.展开更多
Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design Competition.This study aims to conduct a comprehensive security evaluation of Ballet from the perspective ...Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design Competition.This study aims to conduct a comprehensive security evaluation of Ballet from the perspective of differential-linear(DL)cryptanalysis.Specifically,we present an automated search for the DL distinguishers of Ballet based on MILP/MIQCP.For the versions with block sizes of 128 and 256 bits,we obtain 16 and 22 rounds distinguishers with estimated correlations of 2^(-59.89)and 2^(-116.80),both of which are the publicly longest distinguishers.In addition,this study incorporates the complexity information of key-recovery attacks into the automated model,to search for the optimal key-recovery attack structures based on DL distinguishers.As a result,we mount the key-recovery attacks on 16-round Ballet-128/128,17-round Ballet-128/256,and 21-round Ballet-256/256.The data/time complexities for these attacks are 2^(108.36)/2^(120.36),2^(115.90)/2^(192),and 2^(227.62)/2^(240.67),respectively.展开更多
A Distributed Denial-of-Service(DDoS)attack poses a significant challenge in the digital age,disrupting online services with operational and financial consequences.Detecting such attacks requires innovative and effect...A Distributed Denial-of-Service(DDoS)attack poses a significant challenge in the digital age,disrupting online services with operational and financial consequences.Detecting such attacks requires innovative and effective solutions.The primary challenge lies in selecting the best among several DDoS detection models.This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making(MCDM)techniques to compare and select the most effective models.The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion(FWZIC)and MultiAttribute Boundary Approximation Area Comparison(MABAC)methodologies.FWZIC assigns weights to evaluate criteria,while MABAC compares detection models based on the assessed criteria.The results indicate that the FWZIC approach assigns weights to criteria reliably,with time complexity receiving the highest weight(0.2585)and F1 score receiving the lowest weight(0.14644).Among the models evaluated using the MABAC approach,the Support Vector Machine(SVM)ranked first with a score of 0.0444,making it the most suitable for this work.In contrast,Naive Bayes(NB)ranked lowest with a score of 0.0018.Objective validation and sensitivity analysis proved the reliability of the framework.This study provides a practical approach and insights for cybersecurity practitioners and researchers to evaluate DDoS detection models.展开更多
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.
基金supported by Key Laboratory of Cyberspace Security,Ministry of Education,China。
文摘Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金supported by the National Key Research and Development Program of China (2018YFB0804004)the Foundation of the National Natural Science Foundation of China (61602509)+1 种基金the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61521003)the Key Technologies Research and Development Program of Henan Province of China (172102210615)
文摘Co-residency of virtual machines(VMs) of different tenants on the same physical platform would possibly lead to cross-VM side-channel attacks in the cloud. While most of current countermeasures fail for real or immediate deployment due to their requirement for modification of virtualization structure, we adopt dynamic migration, an inherent mechanism of the cloud platform, as a general defense against this kind of threats. To this end, we first set up a unified practical information leakage model which shows the factors affecting side channels and describes the way they influence the damage due to side-channel attacks. Since migration is adopted to limit the time duration of co-residency, we envision this defense as an optimization problem by setting up an Integer Linear Programming(ILP) to calculate optimal migration strategy, which is intractable due to high computational complexity. Therefore, we approximate the ILP with a baseline genetic algorithm, which is further improved for its optimality and scalability. Experimental results show that our migration-based defense can not only provide excellent security guarantees and affordable performance cost in both theoretical simulation and practical cloud environment, but also achieve better optimality and scalability than previous countermeasures.
基金the National Natural Science Foundation of China(61873057)the Education Department of Jilin Province(JJKH20200118KJ).
文摘This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control functions of a cyber network and power flow characteristics of a power network,a power cyber-physical system model is established.Then,the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied,and a cascading failure analysis process is proposed for the cyber-attack environment.In addition,a vulnerability evaluation index is defined from two perspectives,i.e.,the topology integrity and power network operation characteristics.Moreover,the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyberphysical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index.Finally,an IEEE14-bus power network is selected for constructing a power cyber-physical system.Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks,which affect the ability of the cyber network to suppress the propagation of cascading failures,and expand the scale of the cascading failures.The vulnerability evaluation index is calculated for each node,so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.
基金This work was supported by the National Natural Science Foundation of China(No.61379151,61401512,61572052,U1636219)the National Key Research and Development Program of China(No.2016YFB0801303,2016QY01W0105)the Key Technologies Research and Development Program of Henan Provinces(No.162102210032).
文摘In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method,this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical detection.For the spatial image,this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded domain.Then construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography,and use the minimum distortion coding to realize the embedding of the secret messages.Finally,according to the embedding modification amplitude of secret messages in the new embedded domain,the quantization index modulation algorithm is applied to realize the final embedding of secret messages in the original embedded domain.The experimental results show that the algorithm proposed is robust to the three common interpolation attacks including the nearest neighbor interpolation,the bilinear interpolation and the bicubic interpolation.And the average correct extraction rate of embedded messages increases from 50%to over 93% after 0.5 times-fold scaling attack using the bicubic interpolation method,compared with the classical steganography algorithm S-UNIWARD.Also the algorithm proposed has higher detection resistance than the original watermarking algorithm based on quantization index modulation.
基金National Natural Science Foundation of China(No.61272432)Qingdao Science and Technology Development Plan(No.12-1-4-6-(10)-jch)
文摘Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.
基金supported by Systematic Major Project of China State Railway Group Corporation Limited(Grant Number:P2023W002).
文摘The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.
文摘Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectively.But they are susceptible to malicious attacks,which mainly targets particular significant nodes.Therefore,the robustness of the network becomes important for ensuring the network security.This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization(RHAFS-SA)Algorithm.It is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree distribution.The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm(IAFSA)by the simulated annealing(SA)algorithm.The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate.For experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks.The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.
文摘对于多通道软件无线电设备,解决设备内部网络与外部不安全网络间的多路并行数据高速传输问题是一项设计难点。基于此,提出一种基于软件无线电软件通信体系架构(Software Communication Architecture,SCA)硬件抽象层标准的多模式、高速率接口适配模块设计方法,通过多通道的虚拟化接口设计,实现多波形业务数据并行数据流的复接与分发。经过平台验证,本设计支持总吞吐量不低于12 Gb/s的多路并行业务数据传输,可满足多通道、多模式下软件无线电波形的并行数据复接与分发需求。
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
文摘Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification.Some of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these features.Therefore,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions.The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks.The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its robustness.For this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this sense.Sensitivity,luminance,inter and intra-block contrast are used to adjust the JND values.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
基金supported in part by Shanghai Rising-Star Program,China under grant 22QA1409400in part by National Natural Science Foundation of China under grant 62473287 and 62088101in part by Shanghai Municipal Science and Technology Major Project under grant 2021SHZDZX0100.
文摘This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.
基金National Natural Science Foundation of China(62272147,12471492,62072161,12401687)Shandong Provincial Natural Science Foundation(ZR2024QA205)+1 种基金Science and Technology on Communication Security Laboratory Foundation(6142103012207)Innovation Group Project of the Natural Science Foundation of Hubei Province of China(2023AFA021)。
文摘Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design Competition.This study aims to conduct a comprehensive security evaluation of Ballet from the perspective of differential-linear(DL)cryptanalysis.Specifically,we present an automated search for the DL distinguishers of Ballet based on MILP/MIQCP.For the versions with block sizes of 128 and 256 bits,we obtain 16 and 22 rounds distinguishers with estimated correlations of 2^(-59.89)and 2^(-116.80),both of which are the publicly longest distinguishers.In addition,this study incorporates the complexity information of key-recovery attacks into the automated model,to search for the optimal key-recovery attack structures based on DL distinguishers.As a result,we mount the key-recovery attacks on 16-round Ballet-128/128,17-round Ballet-128/256,and 21-round Ballet-256/256.The data/time complexities for these attacks are 2^(108.36)/2^(120.36),2^(115.90)/2^(192),and 2^(227.62)/2^(240.67),respectively.
文摘A Distributed Denial-of-Service(DDoS)attack poses a significant challenge in the digital age,disrupting online services with operational and financial consequences.Detecting such attacks requires innovative and effective solutions.The primary challenge lies in selecting the best among several DDoS detection models.This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making(MCDM)techniques to compare and select the most effective models.The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion(FWZIC)and MultiAttribute Boundary Approximation Area Comparison(MABAC)methodologies.FWZIC assigns weights to evaluate criteria,while MABAC compares detection models based on the assessed criteria.The results indicate that the FWZIC approach assigns weights to criteria reliably,with time complexity receiving the highest weight(0.2585)and F1 score receiving the lowest weight(0.14644).Among the models evaluated using the MABAC approach,the Support Vector Machine(SVM)ranked first with a score of 0.0444,making it the most suitable for this work.In contrast,Naive Bayes(NB)ranked lowest with a score of 0.0018.Objective validation and sensitivity analysis proved the reliability of the framework.This study provides a practical approach and insights for cybersecurity practitioners and researchers to evaluate DDoS detection models.
