Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks

Intrusion in wireless sensor networks (WSNs) aims to degrade or even eliminating the capability of these networks to provide its functions. In this paper, an enhanced intrusion detection system (IDS) is proposed by using the modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). The GWOSVM-IDS used 3 wolves, 5 wolves and 7 wolves to find the best number of wolves. The proposed method aims to increase intrusion detection accuracy and detection rate and reduce processing time in the WSN environment through decrease false alarms rates, and the number of features resulted from the IDSs in the WSN environment. Indeed, the NSL KDD’99 dataset is used to demonstrate the performance of the proposed method and compare it with other existing methods. The proposed methods are evaluated in terms of accuracy, the number of features, execution time, false alarm rate, and detection rate. The results showed that the proposed GWOSVM-IDS with seven wolves overwhelms the other proposed and comparative algorithms.

[1]  Mohammed Feham,et al.  Novel hybrid intrusion detection system for clustered wireless sensor network , 2011, ArXiv.

[2]  Shadi Aljawarneh,et al.  Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model , 2017, J. Comput. Sci..

[3]  Yixian Yang,et al.  A distance sum-based hybrid method for intrusion detection , 2013, Applied Intelligence.

[4]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[5]  NavimipourNima Jafari,et al.  Deployment strategies in the wireless sensor network , 2016 .

[6]  Mohamed Touahria,et al.  Feature selection for intrusion detection using new multi-objective estimation of distribution algorithms , 2019, Applied Intelligence.

[7]  Laith Abualigah,et al.  Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications , 2020, Neural Computing and Applications.

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

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

[10]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[11]  Seyedali Mirjalili,et al.  A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification , 2019, Algorithms for Intelligent Systems.

[12]  V. Jaiganesh,et al.  Intrusion Detection Systems: A Survey and Analysis of Classification Techniques , 2013 .

[13]  Qin Yu,et al.  An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks , 2016, Int. J. Distributed Sens. Networks.

[14]  Laith Mohammad Abualigah,et al.  Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering , 2017, The Journal of Supercomputing.

[15]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[16]  Lior Rokach,et al.  A Survey of Feature Selection Techniques , 2009, Encyclopedia of Data Warehousing and Mining.

[17]  Hossam S. Hassanein,et al.  On The Reliability of Wireless Sensor Networks , 2006, 2006 IEEE International Conference on Communications.

[18]  Laith Mohammad Abualigah,et al.  Hybrid clustering analysis using improved krill herd algorithm , 2018, Applied Intelligence.

[19]  Daniel Curiac,et al.  Wireless Sensor Network Security Enhancement Using Directional Antennas: State of the Art and Research Challenges , 2016, Sensors.

[20]  RehmaniMubashir Husain,et al.  Applications of wireless sensor networks for urban areas , 2016 .

[21]  Somnuk Phon-Amnuaisuk,et al.  A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection , 2010, Applied Intelligence.

[22]  Winston Khoon Guan Seah,et al.  Reliability in wireless sensor networks: A survey and challenges ahead , 2015, Comput. Networks.

[23]  Fatin Norsyafawati Mohd Sabri,et al.  Identifying False Alarm Rates for Intrusion Detection System with Data Mining , 2011 .

[24]  Kien A. Hua,et al.  Decision tree classifier for network intrusion detection with GA-based feature selection , 2005, ACM Southeast Regional Conference.

[25]  Gerhard P. Hancke,et al.  Security in software-defined wireless sensor networks: Threats, challenges and potential solutions , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[26]  Ünal Çavusoglu,et al.  A new hybrid approach for intrusion detection using machine learning methods , 2019, Applied Intelligence.

[27]  Carlos F. García-Hernández,et al.  Wireless Sensor Networks and Applications: a Survey , 2007 .

[28]  Xin-She Yang,et al.  BBA: A Binary Bat Algorithm for Feature Selection , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[29]  Laith Mohammad Abualigah,et al.  Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering , 2018, Studies in Computational Intelligence.

[30]  Ahmad M. Khasawneh,et al.  Void Aware Routing Protocols in Underwater Wireless Sensor Networks: Variants and challenges , 2020, Journal of Physics: Conference Series.

[31]  Ali Diabat,et al.  A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments , 2020, Cluster Computing.

[32]  Bruce A. Draper,et al.  Feature selection from huge feature sets , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[33]  David A. Bell,et al.  A Formalism for Relevance and Its Application in Feature Subset Selection , 2000, Machine Learning.

[34]  Xiangjian He,et al.  Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm , 2016, IEEE Transactions on Computers.

[35]  Syed Mahfuzul Aziz,et al.  Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare , 2015, Sensors.

[36]  Nima Jafari Navimipour,et al.  Deployment strategies in the wireless sensor network: A comprehensive review , 2016, Comput. Commun..

[37]  Mubashir Husain Rehmani,et al.  Applications of wireless sensor networks for urban areas: A survey , 2016, J. Netw. Comput. Appl..

[38]  Aboul Ella Hassanien,et al.  Binary grey wolf optimization approaches for feature selection , 2016, Neurocomputing.

[39]  Iftikhar Ahmad,et al.  Feature Selection Using Particle Swarm Optimization in Intrusion Detection , 2015, Int. J. Distributed Sens. Networks.

[40]  Bamidele Adebisi,et al.  A Wireless Sensor Network Border Monitoring System: Deployment Issues and Routing Protocols , 2017, IEEE Sensors Journal.

[41]  Nerijus Paulauskas,et al.  Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset , 2017, 2017 Open Conference of Electrical, Electronic and Information Sciences (eStream).

[42]  R. C. Suganthe,et al.  Feature Selection in Intrusion Detection Grey Wolf Optimizer , 2017 .

[43]  Yogendra Kumar Jain,et al.  Min Max Normalization Based Data Perturbation Method for Privacy Protection , 2011 .

[44]  Zhongwei Li,et al.  Multi-agent trust-based intrusion detection scheme for wireless sensor networks , 2017, Comput. Electr. Eng..