This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional ...This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly.To defend and prevent such attacks,the first step is to detect them.The current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base station.As a result,overhead and communication costs increased.In this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer.As a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks.Furthermore,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB.With a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.展开更多
In wireless sensor networks （WSNs）, as the shared nature of the wireless medium, jam- ming attacks can be easily launched and result in a great damage to the network. How to deal with jamming attacks has become a gr...In wireless sensor networks （WSNs）, as the shared nature of the wireless medium, jam- ming attacks can be easily launched and result in a great damage to the network. How to deal with jamming attacks has become a great concern recently. Finding the location of a jammer is important to take security actions against the jammer, and thus to restore the network communication. After a comprehensive study on the jammer localization problem, a lightweight easy-operated algorithm called triple circles localization （TCL） is proposed. The evaluation results have demonstrated that, compared with other approaches, TCL achieves the best jammer localization accuracy under variable conditions.展开更多
基金funded by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia through the Project Number (IF-PSAU-2021/01/18707).
文摘This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly.To defend and prevent such attacks,the first step is to detect them.The current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base station.As a result,overhead and communication costs increased.In this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer.As a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks.Furthermore,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB.With a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.
文摘In wireless sensor networks （WSNs）, as the shared nature of the wireless medium, jam- ming attacks can be easily launched and result in a great damage to the network. How to deal with jamming attacks has become a great concern recently. Finding the location of a jammer is important to take security actions against the jammer, and thus to restore the network communication. After a comprehensive study on the jammer localization problem, a lightweight easy-operated algorithm called triple circles localization （TCL） is proposed. The evaluation results have demonstrated that, compared with other approaches, TCL achieves the best jammer localization accuracy under variable conditions.
