针对无线体域网(Wireless Body Area Network,WBAN)面临的能源供应限制和数据时效性不足问题,文中设计了一种基于无线供能体域网(Wireless-powered Body Area Network,WP-BAN)的传输优化策略。对WP-BAN进行系统建模,考虑了基于马尔可夫...针对无线体域网(Wireless Body Area Network,WBAN)面临的能源供应限制和数据时效性不足问题,文中设计了一种基于无线供能体域网(Wireless-powered Body Area Network,WP-BAN)的传输优化策略。对WP-BAN进行系统建模,考虑了基于马尔可夫的数据采集传输过程、能量供应与消耗过程以及信息年龄(Age-of-Information,AoI)增长过程,并在队列稳定性约束和链路容量约束下构建一个关于数据采样速率与数据丢弃率系统效用最大化的离散时间随机优化问题。运用Lyapunov优化理论将原问题转化为单时隙多序列的确定性优化问题,设计了效用最大化的传输调度和能量收集策略(Utility-maximization Transmission Scheduling and Energy Harvesting Strategy,UMTEHS)。在Lyapunov求解框架下使用BARON(Branch and Reduce Optimization Navigator)求解器求解转化后的优化问题,根据当前时刻的队列状态优化下一个时刻的数据传输调度和能量收集率。仿真实验验证了所提策略的有效性,其在保持系统能量中性运行的同时能够获得更大的系统效用和更小的AoI。展开更多
针对命名数据网络(named data networking,NDN)中缓存冗余高、资源利用率低的问题,提出基于数据冗余包的协同缓存放置策略(cooperative cache placement strategy based on da-ta redundancy packet,CCPS-DRP)。该策略先对网络进行社区...针对命名数据网络(named data networking,NDN)中缓存冗余高、资源利用率低的问题,提出基于数据冗余包的协同缓存放置策略(cooperative cache placement strategy based on da-ta redundancy packet,CCPS-DRP)。该策略先对网络进行社区划分,再利用复杂网络中节点介数确定关键路由节点,将数据冗余包中的相同数据副本存放于其中,并动态管理冗余数据副本,实现关键节点集中管理与动态冗余清理的协同机制,以减少相同数据内容的重复缓存,使路由节点中的缓存容量得到充分利用,降低网络负载。实验结果表明,无论在均匀分布或高度偏斜分布的场景下,还是在严格资源限制或有较高资源的场景下,CCPS-DRP在缓存命中率和平均请求时延方面的性能均明显优于常见的随处缓存策略、概率缓存策略等,其通过协同缓存与冗余控制显著提升了网络性能。展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.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
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
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
文摘针对无线体域网(Wireless Body Area Network,WBAN)面临的能源供应限制和数据时效性不足问题,文中设计了一种基于无线供能体域网(Wireless-powered Body Area Network,WP-BAN)的传输优化策略。对WP-BAN进行系统建模,考虑了基于马尔可夫的数据采集传输过程、能量供应与消耗过程以及信息年龄(Age-of-Information,AoI)增长过程,并在队列稳定性约束和链路容量约束下构建一个关于数据采样速率与数据丢弃率系统效用最大化的离散时间随机优化问题。运用Lyapunov优化理论将原问题转化为单时隙多序列的确定性优化问题,设计了效用最大化的传输调度和能量收集策略(Utility-maximization Transmission Scheduling and Energy Harvesting Strategy,UMTEHS)。在Lyapunov求解框架下使用BARON(Branch and Reduce Optimization Navigator)求解器求解转化后的优化问题,根据当前时刻的队列状态优化下一个时刻的数据传输调度和能量收集率。仿真实验验证了所提策略的有效性,其在保持系统能量中性运行的同时能够获得更大的系统效用和更小的AoI。
文摘针对命名数据网络(named data networking,NDN)中缓存冗余高、资源利用率低的问题,提出基于数据冗余包的协同缓存放置策略(cooperative cache placement strategy based on da-ta redundancy packet,CCPS-DRP)。该策略先对网络进行社区划分,再利用复杂网络中节点介数确定关键路由节点,将数据冗余包中的相同数据副本存放于其中,并动态管理冗余数据副本,实现关键节点集中管理与动态冗余清理的协同机制,以减少相同数据内容的重复缓存,使路由节点中的缓存容量得到充分利用,降低网络负载。实验结果表明,无论在均匀分布或高度偏斜分布的场景下,还是在严格资源限制或有较高资源的场景下,CCPS-DRP在缓存命中率和平均请求时延方面的性能均明显优于常见的随处缓存策略、概率缓存策略等,其通过协同缓存与冗余控制显著提升了网络性能。
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
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.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
