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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.

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  • Abdollahzadeh S, Navimipour NJ (2016) Deployment strategies in the wireless sensor network: a comprehensive review. Comput Commun 91:1–16

    Article  Google Scholar 

  • Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

    Book  Google Scholar 

  • Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl.

    Article  Google Scholar 

  • Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Comput.

    Article  Google Scholar 

  • Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  • Ahmad I (2015) Feature selection using particle swarm optimization in intrusion detection. Int J Distrib Sens Netw 11(10):806954

    Google Scholar 

  • Al-Tashi Q, Rais HM, Abdulkadir SJ, Mirjalili S, Alhussian H (2020) A review of grey wolf optimizer-based feature selection methods for classification. In: Evolutionary machine learning techniques. Springer, Singapore, pp 273–286

  • Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160

    Article  Google Scholar 

  • Ambusaidi MA, He X, Nanda P, Tan Z (2016) Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans Comput 65(10):2986–2998

    MathSciNet  Article  Google Scholar 

  • Amiri F, Yousefi MR, Lucas C, Shakery A, Yazdani N (2011) Mutual information-based feature selection for intrusion detection systems. J Netw Comput Appl 34(4):1184–1199

    Article  Google Scholar 

  • Bell DA, Wang H (2000) A formalism for relevance and its application in feature subset selection. Mach Learn 41(2):175–195

    Article  Google Scholar 

  • Benmessahel I, Xie K, Chellal M (2018) A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Appl Intell 48(8):2315–2327

    Article  Google Scholar 

  • Bins J, Draper BA (2001) Feature selection from huge feature sets. In: Proceedings eighth IEEE international conference on computer vision, vol 2. ICCV 2001, IEEE, pp 159–165

  • Çavuşoğlu Ü (2019) A new hybrid approach for intrusion detection using machine learning methods. Appl Intell 49(7):2735–2761

    Article  Google Scholar 

  • Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Article  Google Scholar 

  • Chelli K (2015) Security issues in wireless sensor networks: attacks and countermeasures. In: Proceedings of the world congress on engineering, vol 1, issue 20

  • Chizi B, Rokach L, Maimon O (2009) A survey of feature selection techniques. In: Encyclopedia of data warehousing and mining, second edition. IGI Global, pp 1888–1895

  • Curiac DI (2016) Wireless sensor network security enhancement using directional antennas: state of the art and research challenges. Sensors 16(4):488

    Article  Google Scholar 

  • Devi EM, Suganthe RC (2017) Feature selection in intrusion detection grey wolf optimizer. Asian J Res Soc Sci Human 7(3):671–682

    Google Scholar 

  • Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  • García-Hernández CF, Ibarguengoytia-Gonzalez PH, García-Hernández J, Pérez-Díaz JA (2007) Wireless sensor networks and applications: a survey. IJCSNS Int J Comput Sci Netw Secur 7(3):264–273

    Google Scholar 

  • Guo C, Zhou Y, Ping Y, Zhang Z, Liu G, Yang Y (2014) A distance sum-based hybrid method for intrusion detection. Appl Intell 40(1):178–188

    Article  Google Scholar 

  • Hammoudeh M, Al-Fayez F, Lloyd H, Newman R, Adebisi B, Bounceur A, Abuarqoub A (2017) A wireless sensor network border monitoring system: deployment issues and routing protocols. IEEE Sens J 17(8):2572–2582

    Article  Google Scholar 

  • Haque S, Rahman M, Aziz S (2015) Sensor anomaly detection in wireless sensor networks for healthcare. Sensors 15(4):8764–8786

    Article  Google Scholar 

  • Jaiganesh V, Mangayarkarasi S, Sumathi P (2013) Intrusion detection systems: a survey and analysis of classification techniques. Int J Adv Res Comput Commun Eng 2(4):1629–1635

    Google Scholar 

  • Jain YK, Bhandare SK (2011) Min max normalization based data perturbation method for privacy protection. Int J Comput Commun Technol 2(8):45–50

    Google Scholar 

  • Jin X, Liang J, Tong W, Lu L, Li Z (2017) Multi-agent trust-based intrusion detection scheme for wireless sensor networks. Comput Electr Eng 59:262–273

    Article  Google Scholar 

  • Khasawneh AM, Abualigah L, Al Shinwan M (2020) Void aware routing protocols in underwater wireless sensor networks: variants and challenges. J. Phys Conf Ser 1550(3):032145

    Article  Google Scholar 

  • Khor KC, Ting CY, Phon-Amnuaisuk S (2012) A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl Intell 36(2):320–329

    Article  Google Scholar 

  • Mahmood MA, Seah WK, Welch I (2015) Reliability in wireless sensor networks: a survey and challenges ahead. Comput Netw 79:166–187

    Article  Google Scholar 

  • Maleh Y, Ezzati A (2015) Lightweight intrusion detection scheme for wireless sensor networks. IAENG Int J Comput Sci 42(4):347–354

    Google Scholar 

  • Maza S, Touahria M (2019) Feature selection for intrusion detection using new multi-objective estimation of distribution algorithms. Appl Intell 49(12):4237–4257

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Nakamura RY, Pereira LA, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI conference on graphics, patterns and images. IEEE, pp 291–297

  • Paulauskas N, Auskalnis J (2017) Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset. In: 2017 open conference of electrical, electronic and information sciences (eStream). IEEE, pp 1–5

  • Pritchard SW, Hancke GP, Abu-Mahfouz AM (2017) Security in software-defined wireless sensor networks: threats, challenges and potential solutions. In: 2017 IEEE 15th international conference on industrial informatics (INDIN). IEEE, pp 168–173

  • Rashid B, Rehmani MH (2016) Applications of wireless sensor networks for urban areas: a survey. J Netw Comput Appl 60:192–219

    Article  Google Scholar 

  • Sabri FNM, Norwawi NM, Seman K (2011) Identifying false alarm rates for intrusion detection system with data mining. IJCSNS Int J Comput Sci Netw Secur 11(4):95

    Google Scholar 

  • Sedjelmaci H, Feham M (2011) Novel hybrid intrusion detection system for clustered wireless sensor network. arXiv preprint arXiv:1108.2656

  • Stein G, Chen B, Wu AS, Hua KA (2005) Decision tree classifier for network intrusion detection with GA-based feature selection. In: Proceedings of the 43rd annual southeast regional conference-volume 2. ACM, pp 136–141

  • Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, pp 1–6

  • Yu Q, Jibin L, Jiang L (2016) An improved ARIMA-based traffic anomaly detection algorithm for wireless sensor networks. Int J Distrib Sens Netw 12(1):9653230

    Article  Google Scholar 

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Correspondence to Laith Abualigah.

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Safaldin, M., Otair, M. & Abualigah, L. Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J Ambient Intell Human Comput 12, 1559–1576 (2021).

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  • Intrusion detection system
  • Binary grey wolf optimizer
  • Wireless sensor networks
  • Intrusions