Wireless sensor network intrusion detection system based on sequence model
CHENG Xiaohui1,2, NIU Tong1, WANG Yanjun1
1. College of Information Science and Engineering, Guilin University of Technology, Guilin Guangxi 541006, China 2. Guangxi Key Laboratory of Embedded Technology and Intelligent System (Guilin University of Technology), Guilin Guangxi 541006, China
Abstract:With the rapid development of Internet of Things (IoT), more and more IoT node devices are deployed, but the accompanying security problem cannot be ignored. Node devices at the network layer of IoT mainly communicate through wireless sensor networks. Compared with the Internet, they are more open and more vulnerable to network attacks such as denial of service. Aiming at the network layer security problem faced by wireless sensor networks, a network intrusion detection system based on sequence model was proposed to detect and alarm the network layer intrusion, which achieved higher recognition rate and lower false positive rate. Besides, aiming at the security problem of the node host device faced by wireless sensor network node devices, with the consideration of the node overhead, a host intrusion detection system based on simple sequence model was proposed. The experimental results show that, the two intrusion detection systems for the network layer and the host layer of wireless sensor network both have the accuracy more than 99%, and the false detection rate about 1%, which meet the industrial requirements. These two proposed systems can comprehensively and effectively protect the wireless sensor network security.
1 LIUZ, AZARDERAKHSHR, KIMH, et al. Efficient software implementation of ring-LWE encryption on IoT processors[J]. IEEE Transactions on Computers, 2017(Early Access):1-1. 2 潘建国,李豪. 基于实用拜占庭容错的物联网入侵检测方法[J]. 计算机应用, 2019, 39(6):1742-1746. PANJ G, LIH. Intrusion detection approach for IoT based on practical Byzantine fault tolerance[J]. Journal of Computer Applications, 2019, 39(6):1742-1746. 3 刘建. 基于改进神经网络的网络入侵检测[J]. 科技创新与应用, 2018(2):11-12, 14. LIUJ. Network intrusion detection based on improved neural network[J]. Technology Innovation and Application, 2018, 2:11-12, 14. 4 刘达. 基于朴素贝叶斯分类算法的数据库入侵检测系统[J]. 网络空间安全, 2017, 8(8/9):32-34. LIUD. An intrusion detection system based on naive Bayes classifier[J]. Cyberspace Security, 2017, 8(8/9):32-34. 5 MIR A, KHACHANEA. Sensing harmful gases in industries using IOT and WSN[C]// Proceedings of the 4th International Conference on Computing Communication Control and Automation. Piscataway: IEEE, 2018:1-3. 6 阙宏宇,梁波.入侵检测技术网络安全中的具体运用[J]. 电子技术与软件工程,2017(11):205. QUEH Y, LIANGB. Specific application of intrusion detection technology in network security[J]. Electronic Technology and Software Engineering, 2017(11): 205. 7 MODARESH, SALLEHR, MORAVEJOSHARIEHA. Overview of security issues in wireless sensor networks[C]// Proceedings of the 3rd International Conference on Computational Intelligence, Modelling and Simulation. Piscataway: IEEE, 2011: 308-311. 8 KIMD S, NGUYENH N, PARKJ S. Genetic algorithm to improve SVM based network intrusion detection system[C]// Proceedings of the 19th International Conference on Advanced Information Networking and Applications. Piscataway: IEEE, 2005: 155-158. 9 BONTEMPSL, CAOV L, MCDERMOTTJ, et al. Collective anomaly detection based on long short term memory recurrent neural network[C]// Proceedings of the 2016 International Conference on Future Data and Security Engineering, LNCS 10018. Cham: Springer, 2016:141-152. 10 HODOE, BELLEKENSX, HAMILTONA, et al. Threat analysis of IoT networks using artificial neural network intrusion detection system[J]. Tetrahedron Letters, 2017, 42(39):6865-6867. 11 ALMOMANII, AL-KASASBEHB, AL-AKHRASM. WSN-DS: a dataset for intrusion detection systems in wireless sensor networks[J]. Journal of Sensors, 2016, 2016: Article No.4731953. 12 BRIDGESR A, GLASS-VANDERLANT R, IANNACONEM D, et al. A survey of intrusion detection systems leveraging host data[J]. ACM Computing Surveys, 2018, 52(6):Article No.128. 13 CREECHG, HUJ. A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns[J]. IEEE Transactions on Computers, 2014, 63(4): 807-819. 14 SUBBAB, BISWASS, KARMAKARS. Host based intrusion detection system using frequency analysis of n-gram terms[C]// Proceedings of the 2017 IEEE Region 10 Conference. Piscataway: IEEE, 2017: 2006-2011. 15 YEQ, YANGX, CHENC, et al. River water quality parameters prediction method based on LSTM-RNN model[C]// Proceedings of the 2019 Chinese Control and Decision Conference. Piscataway: IEEE, 2019: 3024-3028.