提出一种基于几何覆盖理论的Jamming攻击定位(GCL,geometry-covering based localization)算法。GCL算法利用计算几何中的凸壳理论,特别是最小包容圆方法,对Jamming攻击者进行定位。理论证明了该算法的正确性和较低的时间复杂度(O(nlogn...提出一种基于几何覆盖理论的Jamming攻击定位(GCL,geometry-covering based localization)算法。GCL算法利用计算几何中的凸壳理论,特别是最小包容圆方法,对Jamming攻击者进行定位。理论证明了该算法的正确性和较低的时间复杂度(O(nlogn));模拟实验表明,该算法在攻击者攻击范围、网络节点密度以及攻击者位置等度量值变化的情况下,比已有算法具有更好的定位准确度。展开更多
Internet of Things (IoT) networks present unique cybersecurity challenges due to their distributed and heterogeneous nature. Our study explores the effectiveness of two types of deep learning models, long-term memory ...Internet of Things (IoT) networks present unique cybersecurity challenges due to their distributed and heterogeneous nature. Our study explores the effectiveness of two types of deep learning models, long-term memory neural networks (LSTMs) and deep neural networks (DNNs), for detecting attacks in IoT networks. We evaluated the performance of six hybrid models combining LSTM or DNN feature extractors with classifiers such as Random Forest, k-Nearest Neighbors and XGBoost. The LSTM-RF and LSTM-XGBoost models showed lower accuracy variability in the face of different types of attack, indicating greater robustness. The LSTM-RF and LSTM-XGBoost models show variability in results, with accuracies between 58% and 99% for attack types, while LSTM-KNN has higher but more variable accuracies, between 72% and 99%. The DNN-RF and DNN-XGBoost models show lower variability in their results, with accuracies between 59% and 99%, while DNN-KNN has higher but more variable accuracies, between 71% and 99%. LSTM-based models are proving to be more effective for detecting attacks in IoT networks, particularly for sophisticated attacks. However, the final choice of model depends on the constraints of the application, taking into account a trade-off between accuracy and complexity.展开更多
物联网通过采用认知无线电的动态频谱共享机制提高了频谱利用率,然而认知物联网(Cognitive Internet of Things,CIoT)容易受到多种攻击,包括干扰攻击和窃听攻击。首先,基于联盟博弈考虑一个合作模型,其中合法用户合作传输以提高信干噪比...物联网通过采用认知无线电的动态频谱共享机制提高了频谱利用率,然而认知物联网(Cognitive Internet of Things,CIoT)容易受到多种攻击,包括干扰攻击和窃听攻击。首先,基于联盟博弈考虑一个合作模型,其中合法用户合作传输以提高信干噪比(Signal to Interference plus Noise Ratio,SINR),而干扰机合作以提高接收信号强度(Jammer Received Signal Strength,JRSS),窃听机旨在降低系统的保密速率。其次,基于演化博弈论研究了CIoT网络中合法用户和攻击者的动态特性,利用能量采集(Energy Harvesting,EH)技术提高用户的发射功率以提高SINR,从而提高用户的合作水平。此外,通过设置协作干扰节点劣化窃听信道以提高系统的保密速率。仿真结果表明,所提方法在应对干扰攻击和窃听攻击问题上是有效的,且在SINR和保密速率方面优于传统方法。展开更多
提出一种面向多跳无线网络的多干扰源定位算法,主要包括3个步骤:基于梯度下降法的分组投递率谷点推定、基于梯度上升法的接收干扰强度(RJSS,received jamming signal strength)峰点推定和聚类分析。首先,算法从多个初始节点出发,采用梯...提出一种面向多跳无线网络的多干扰源定位算法,主要包括3个步骤:基于梯度下降法的分组投递率谷点推定、基于梯度上升法的接收干扰强度(RJSS,received jamming signal strength)峰点推定和聚类分析。首先,算法从多个初始节点出发,采用梯度下降法,沿着分组投递率梯度下降最快的方向逼近干扰源,直至到达分组投递率谷点;然后应用功率自适应动态调整技术,采用梯度上升法,沿着接收干扰强度上升最快的方向继续逼近干扰源,直至接收干扰强度峰点(也称为RJSS停止节点);最后通过对无法与RJSS停止节点通信的邻居节点进行聚类分析,确定干扰源的数量和位置。模拟实验表明,与现有算法相比,所提算法可以有效降低多干扰源定位过程的定位误差;并且,当干扰源间距符合限定条件时,算法定位结果更优。展开更多
Evaluation of IEEE 802.11 Mobile Ad Hoc Networks (MANET) security issues becomes significant concern for researchers since Denial of Service (DoS) attacks are recognized as one of the most harmful threats. A variety o...Evaluation of IEEE 802.11 Mobile Ad Hoc Networks (MANET) security issues becomes significant concern for researchers since Denial of Service (DoS) attacks are recognized as one of the most harmful threats. A variety of security mechanisms are proposed to solve security dilemma in MANETs against different layers of DoS attacks. Physical Layer jamming attacks exhaust the victim’s network resources such as bandwidth, computing power, battery, etc. Unified Security Mechanism (USM) and Rate Adaptation Scheme (RAS) are two of the proposed methods by researchers against DoS attacks. USM and RAS mechanisms are simulated through OPNET simulator and Jamming Attack is generated on the network for each security mechanisms to compare specific performance metrics on the network.展开更多
The 802.15.4 Wireless Sensor Networks (WSN) becomes more economical, feasible and sustainable for new generation communication environment, however their limited resource constraints such as limited power capacity mak...The 802.15.4 Wireless Sensor Networks (WSN) becomes more economical, feasible and sustainable for new generation communication environment, however their limited resource constraints such as limited power capacity make them difficult to detect and defend themselves against variety of attacks. The radio interference attacks that generate for WSN at the Physical Layer cannot be defeated through conventional security mechanisms proposed for 802.15.4 standards. The first section introduces the deployment model of two-tier hierarchical cluster topology architecture and investigates different jamming techniques proposed for WSN by creating specific classification of different types of jamming attacks. The following sections expose the mitigation techniques and possible built-in mechanisms to mitigate the link layer jamming attacks on proposed two-tier hierarchical clustered WSN topology. The two-tier hierarchical cluster based topology is investigated based on contention based protocol suite through OPNET simulation scenarios.展开更多
文摘提出一种基于几何覆盖理论的Jamming攻击定位(GCL,geometry-covering based localization)算法。GCL算法利用计算几何中的凸壳理论,特别是最小包容圆方法,对Jamming攻击者进行定位。理论证明了该算法的正确性和较低的时间复杂度(O(nlogn));模拟实验表明,该算法在攻击者攻击范围、网络节点密度以及攻击者位置等度量值变化的情况下,比已有算法具有更好的定位准确度。
文摘Internet of Things (IoT) networks present unique cybersecurity challenges due to their distributed and heterogeneous nature. Our study explores the effectiveness of two types of deep learning models, long-term memory neural networks (LSTMs) and deep neural networks (DNNs), for detecting attacks in IoT networks. We evaluated the performance of six hybrid models combining LSTM or DNN feature extractors with classifiers such as Random Forest, k-Nearest Neighbors and XGBoost. The LSTM-RF and LSTM-XGBoost models showed lower accuracy variability in the face of different types of attack, indicating greater robustness. The LSTM-RF and LSTM-XGBoost models show variability in results, with accuracies between 58% and 99% for attack types, while LSTM-KNN has higher but more variable accuracies, between 72% and 99%. The DNN-RF and DNN-XGBoost models show lower variability in their results, with accuracies between 59% and 99%, while DNN-KNN has higher but more variable accuracies, between 71% and 99%. LSTM-based models are proving to be more effective for detecting attacks in IoT networks, particularly for sophisticated attacks. However, the final choice of model depends on the constraints of the application, taking into account a trade-off between accuracy and complexity.
文摘物联网通过采用认知无线电的动态频谱共享机制提高了频谱利用率,然而认知物联网(Cognitive Internet of Things,CIoT)容易受到多种攻击,包括干扰攻击和窃听攻击。首先,基于联盟博弈考虑一个合作模型,其中合法用户合作传输以提高信干噪比(Signal to Interference plus Noise Ratio,SINR),而干扰机合作以提高接收信号强度(Jammer Received Signal Strength,JRSS),窃听机旨在降低系统的保密速率。其次,基于演化博弈论研究了CIoT网络中合法用户和攻击者的动态特性,利用能量采集(Energy Harvesting,EH)技术提高用户的发射功率以提高SINR,从而提高用户的合作水平。此外,通过设置协作干扰节点劣化窃听信道以提高系统的保密速率。仿真结果表明,所提方法在应对干扰攻击和窃听攻击问题上是有效的,且在SINR和保密速率方面优于传统方法。
文摘提出一种面向多跳无线网络的多干扰源定位算法,主要包括3个步骤:基于梯度下降法的分组投递率谷点推定、基于梯度上升法的接收干扰强度(RJSS,received jamming signal strength)峰点推定和聚类分析。首先,算法从多个初始节点出发,采用梯度下降法,沿着分组投递率梯度下降最快的方向逼近干扰源,直至到达分组投递率谷点;然后应用功率自适应动态调整技术,采用梯度上升法,沿着接收干扰强度上升最快的方向继续逼近干扰源,直至接收干扰强度峰点(也称为RJSS停止节点);最后通过对无法与RJSS停止节点通信的邻居节点进行聚类分析,确定干扰源的数量和位置。模拟实验表明,与现有算法相比,所提算法可以有效降低多干扰源定位过程的定位误差;并且,当干扰源间距符合限定条件时,算法定位结果更优。
文摘Evaluation of IEEE 802.11 Mobile Ad Hoc Networks (MANET) security issues becomes significant concern for researchers since Denial of Service (DoS) attacks are recognized as one of the most harmful threats. A variety of security mechanisms are proposed to solve security dilemma in MANETs against different layers of DoS attacks. Physical Layer jamming attacks exhaust the victim’s network resources such as bandwidth, computing power, battery, etc. Unified Security Mechanism (USM) and Rate Adaptation Scheme (RAS) are two of the proposed methods by researchers against DoS attacks. USM and RAS mechanisms are simulated through OPNET simulator and Jamming Attack is generated on the network for each security mechanisms to compare specific performance metrics on the network.
文摘The 802.15.4 Wireless Sensor Networks (WSN) becomes more economical, feasible and sustainable for new generation communication environment, however their limited resource constraints such as limited power capacity make them difficult to detect and defend themselves against variety of attacks. The radio interference attacks that generate for WSN at the Physical Layer cannot be defeated through conventional security mechanisms proposed for 802.15.4 standards. The first section introduces the deployment model of two-tier hierarchical cluster topology architecture and investigates different jamming techniques proposed for WSN by creating specific classification of different types of jamming attacks. The following sections expose the mitigation techniques and possible built-in mechanisms to mitigate the link layer jamming attacks on proposed two-tier hierarchical clustered WSN topology. The two-tier hierarchical cluster based topology is investigated based on contention based protocol suite through OPNET simulation scenarios.
