Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Org...Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.展开更多
A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a...A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.展开更多
Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing exponentially.Sensitive devices’effectiveness harms end users,increases cyber threats and identity theft,...Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing exponentially.Sensitive devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended periods.Attacks on Internet of Things interfaces must be closely monitored in real time for effective safety and security.Following the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular networks.Even to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 GHz.It gives you the most recent information.Many countries have already established this technology within their border.Security is the most crucial aspect of using a 5G network.Because of the absence of study and network deployment,new technology first introduces new gaps for attackers and hackers.Internet Protocol(IP)attacks and intrusion will become more prevalent in this system.An efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this research.This research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/attackers.After applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.展开更多
基金Researcher Supporting Project number(RSPD2025R582),King Saud University,Riyadh,Saudi Arabia.
文摘Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.
文摘A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.
文摘Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing exponentially.Sensitive devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended periods.Attacks on Internet of Things interfaces must be closely monitored in real time for effective safety and security.Following the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular networks.Even to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 GHz.It gives you the most recent information.Many countries have already established this technology within their border.Security is the most crucial aspect of using a 5G network.Because of the absence of study and network deployment,new technology first introduces new gaps for attackers and hackers.Internet Protocol(IP)attacks and intrusion will become more prevalent in this system.An efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this research.This research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/attackers.After applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.
