Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network

With the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers from malicious attacks, not only the serious loss of information will lead to the paralysis of the Internet of Things equipment. Aiming at the security problem of the Internet of Things, this paper puts forward the LM-BP neural network model. The LM-BP neural network model is applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm is given. LM algorithm has the characteristics of fast optimization speed and strong robustness and uses this characteristic to optimize the weight threshold of traditional BP neural network. Through establishing LM-BP neural network classifier, KDD CUP 99 intrusion detection data set is imported into an LM-BP neural network classifier, and the best results are obtained through continuous training. Finally, the experimental simulation results show that this model has higher detection rate and lower false alarm rate than the traditional BP neural network model and PSO-BP neural network model for DOS, R2L, U2L, and Probing, thus this modified model has certain promotion value.

[1]  S. P. Shantharajah,et al.  A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms , 2015 .

[2]  Luiz Eduardo Soares de Oliveira,et al.  Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems , 2017, IEEE Transactions on Computers.

[3]  Marcin Woźniak,et al.  Algorithm Research of Known-plaintext Attack on Double Random Phase Mask Based on WSNs , 2019 .

[4]  Tao Qin,et al.  IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things , 2019, Sensors.

[5]  Gunasekaran Manogaran,et al.  Intelligent decision-making of online shopping behavior based on internet of things , 2020, Int. J. Inf. Manag..

[6]  Salwani Abdullah,et al.  Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks , 2018, IEEE Access.

[7]  Kun Xie,et al.  A new evolutionary neural networks based on intrusion detection systems using multiverse optimization , 2017, Applied Intelligence.

[8]  Akhilesh Tiwari,et al.  A Rough Set Based Feature Selection on KDD CUP 99 Data Set , 2015 .

[9]  Dean Zhao,et al.  An optimized classification algorithm by BP neural network based on PLS and HCA , 2014, Applied Intelligence.

[10]  Jian Su,et al.  CDMA-based anti-collision algorithm for EPC global C1 Gen2 systems , 2018, Telecommun. Syst..

[11]  Robert C. Atkinson,et al.  Threat analysis of IoT networks using artificial neural network intrusion detection system , 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC).

[12]  Du Xiaowen.  Application of LM-BP neural network in predicting dam deformation , 2011 .

[13]  Jie Li,et al.  Feature Extraction and Area Identification of Wireless Channel in Mobile Communication , 2019 .

[14]  Sanjeev Jain,et al.  Bandwidth Spoofing and Intrusion Detection System for Multistage 5G Wireless Communication Network , 2018, IEEE Transactions on Vehicular Technology.

[15]  Xu Yang,et al.  Research on network intrusion detection based on PCA PSO-BP , 2016 .

[16]  Ahmed I. Saleh,et al.  A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers , 2017, Artificial Intelligence Review.

[17]  Solane Duque,et al.  Using Data Mining Algorithms for Developing a Model for Intrusion Detection System (IDS) , 2015, Complex Adaptive Systems.

[18]  Verónica Bolón-Canedo,et al.  Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset , 2011, Expert Syst. Appl..

[19]  B. Muthukumar,et al.  Intrusion Detection System (IDS): Anomaly Detection Using Outlier Detection Approach , 2015 .

[20]  Harish Kumar,et al.  An intrusion detection system using network traffic profiling and online sequential extreme learning machine , 2015, Expert Syst. Appl..

[21]  Sudhir Kumar Sharma,et al.  Analysis of KDD Dataset Attributes - Class wise for Intrusion Detection , 2015 .

[22]  Song Lin.  Application of mutation particle swarm optimization based BP neural network in the intrusion detection system , 2013 .

[23]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[24]  Wei Tai-yuan.  Study on GPS Elevation Fitting Algorithm Based on LM-BP Neural Network , 2011 .

[25]  Mauro Leonardi,et al.  ADS-B Anomalies and Intrusions Detection by Sensor Clocks Tracking , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Kai Cui,et al.  Research on prediction model of geotechnical parameters based on BP neural network , 2018, Neural Computing and Applications.

[27]  Nasser Yazdani,et al.  Mutual information-based feature selection for intrusion detection systems , 2011, J. Netw. Comput. Appl..

[28]  Hao Wu,et al.  The Application of BP Neural Network in Network Intrusion Detection , 2013 .

[29]  Gang Ke,et al.  The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network , 2014 .

[30]  Sun Wen-jin.  Improved BM Algorithm and Its Application in Network Intrusion Detection , 2013 .

[31]  Kim-Kwang Raymond Choo,et al.  Fair Resource Allocation in an Intrusion-Detection System for Edge Computing: Ensuring the Security of Internet of Things Devices , 2018, IEEE Consumer Electronics Magazine.

[32]  Ahmet Rizaner,et al.  A novel support vector machine based intrusion detection system for mobile ad hoc networks , 2018, Wirel. Networks.