Opeyemi Osanaiye, Olayinka Ogundile, Folayo Aina, Ayodele Periola

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Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment.  Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period.


Chi-squared, cluster, Gain ratio, intrusion detection, NSL-KDD, ReliefF, WSNs

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I. Almomani, B. Al-Kasasbeh, M. AL-Akhras, “WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks”, Journal of Sensors, pp. 1–16, 2016.

C. O'Reilly, A. Gluhak, M. A. Imran, S. Rajasegarar, “Anomaly detection in wireless sensor networks in a non-stationary environment”, IEEE Communications Surveys & Tutorials, vol. 16, pp. 1413–1432, 2014.

O.O. Ogundile, A. S. Alfa (2017), “A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks”, Sensor, vol. 17, 1084, 1–51, 2017.

O. Osanaiye, A. Alfa, “Denial of Service Defence for Resource Availability in Wireless Sensor Networks”, IEEE Access, vol. 6, pp. 6975–7004, 2018.

H.M. Salmon, et al, “Intrusion detection system for wireless sensor networks using danger theory immune-inspired techniques”, International journal of wireless information networks, vol. 20, pp. 39–66, 2013.

V. F. Taylor, D. T. Fokum, “Mitigating black hole attacks in wireless sensor networks using node-resident expert systems”, In Proceedings of the IEEE Wireless Telecommunications Symposium (WTS), pp. 1–7, 2014.

S. Athmani, D.E. Boubiche, A. Bilami, “Hierarchical energy efficient intrusion detection system for black hole attacks in WSNs”, In Proceedings of the IEEE World Congress Computer and Information Technology (WCCIT), pp. 1–5, 2013.

O. Osanaiye, R. Choo, M. Dlodlo, “Distributed Denial of Service (DDoS) Resilience in Cloud: Review and Conceptual Cloud DDoS Mitigation Framework”, Journal of Network and Computer Applications, vol. 69, pp. 1447–1465, 2016.

M. Tavallaee, E. Bagheri, W. Lu, A. Ghorbani, “A detailed analysis of the KDD CUP 99 dataset”, In Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications CISDA, pp. 1–6.

Wang S.-S., Yan K.-Q., Wang S.-C., Liu C.-W. (2011) An integrated intrusion detection system for cluster-based wireless sensor networks. Expert Systems with Applications, 38, 15234–15243.

O. Osanaiye, H. Cai, K.K.R. Choo, A. Dehghantanha, Z. Xu and M. Dlodlo, “Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing” EURASIP Journal on Wireless Communications and Networking, vol. 130, pp. 1–10, 2016.

X. Deng, “An intrusion detection system for cluster based wireless sensor networks”, In Proceedings of the 16th IEEE International Symposium on Wireless Personal Multimedia Communications (WPMC), 2013, pp. 1–5.

K. Q. Yan, S.C. Wang, S.S. Wang, C.W. Liu, “Hybrid Intrusion Detection System for enhancing the security of a cluster-based Wireless Sensor Network”, In Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 1, 2010, pp. 114–118.

K. Medhat, R.A. Ramadan, I. Talkhan, “Distributed Intrusion Detection System for Wireless Sensor Networks”, In Proceedings of the 9th IEEE International Conference on Next Generation Mobile Applications, Services and Technologies, 2015, pp. 234–239.

M. Tiwar, K.V. Arya, R. Choudhari, K. S. Choudhary, “Designing intrusion detection to detect black hole and selective forwarding attack in WSN based on local information”, In Proceedings of the 4th IEEE International Conference on Computer Sciences and Convergence Information Technology ICCIT'09, 2009, pp. 824–828.

A.P.R. Da Silva, et al, “Decentralized intrusion detection in wireless sensor networks” In Proceedings of the 1st ACM international workshop on Quality of service & security in wireless and mobile networks, 2005, pp. 16–23.

Jong K., Marchiori E., Sebag M., van der Vaart A. (2004) Feature Selection in Proteomic Patten Data with Support Vector Machines. Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 41–48.

J. Yick, B. Mukherjee, D. Ghosal, “Wireless sensor network survey”, Computer networks, vol. 52, pp. 2292–2330, 2008.

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “A survey on sensor networks”, IEEE Communications magazine, vol. 40, pp. 102–114, 2002

J. Heidemann, et al, “Research challenges and applications for underwater sensor networking” In Proceedings of the IEEE Wireless Communications and Networking Conference, WCNC, 2006, pp. 228–235.

I. F. Akyildiz, T. Melodia, K. R. Chowdhury, “A survey on wireless multimedia sensor networks”, Computer networks, vol. 51, pp. 921–960, 2007.

A. Abduvaliyev, A.-S. K. Pathan, J. Zhou, R. Roman, W.-C. Wong, “On the vital areas of intrusion detection systems in wireless sensor networks”, IEEE Communications Surveys & Tutorials, vol. 15, pp. 1223–1237, 2013.

Y. Yu, K. Li, W. Zhou, P. Li, “Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures”, Journal of Network and computer Applications, vol. 35, pp. 867–880, 2012.

C.C. Su, K.M. Chang, Y.H. Kuo, M.F. Horng, “The new intrusion prevention and detection approaches for clustering-based sensor networks”, In Proceedings of the IEEE Wireless Communications and Networking Conference, vol. 4, 2015, pp. 1927–1932.

M. H. Anisi, A. H. Abdullah, S. A. Razak, “Energy-efficient and reliable data delivery in wireless sensor networks”, Wireless Networks, vol. 19, pp. 495–505.

P. Kuila, P.K. Jana, “Energy Efficient Load- Balanced Clustering Algorithm for Wireless Sensor Networks”, Procedia Technology, vol. 6, pp. 771–777, 2012.

P. Kuila, S. K. Gupta, P.K. Jana, “A novel evolutionary approach for load balanced clustering problem for wireless sensor networks”, Swarm and Evolutionary Computation, vol. 12, pp. 48–56, 2013.

P. Kuila, P.K. Jana, “Approximation schemes for load balanced clustering in wireless sensor networks”, Journal of Supercomputing, vol. 68, pp. 87–105, 2014.

R. Xie, X. Jia, “Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing”, IEEE Trans. Parallel Distrib. Syst., vol. 25, pp. 806–815, 2014.

O. A. Osanaiye, DDoS defence for service availability in cloud computing. Doctoral dissertation, University of Cape Town, 2016.

V. Bolon-Canedo, N. Sanchez-Marono, A. Alonso-Betanzos, “A review of feature selection methods on synthetic data”, Knowledge and information systems, vol. 34, no. 3, pp. 483–519, 2013.

C. J. Mantas, J. Abellan, “Credal-C4. 5 Decision tree based on imprecise probabilities to classify noisy data”, Expert Systems with Applications, vol. 41, pp. 4625–4637, 2014.

H.F. Eid, A.E. Hassanien, T.H. Kim, S. Banerjee, “Linear correlation-based feature selection for network intrusion detection model”, In Advances in Security of Information and Communication Networks, pp. 240–248, 2013.

M.B. Yassein, Y. Khamayseh, M. AbuJazoh, “Feature Selection for Black Hole Attacks”, Journal of Universal Computer Science, vol. 22, no. 4, pp. 521–536, 2016.

J. Gehrke, V. Ganti, R. Ramakrishnan, W.Y. Loh, “BOAT-optimistic decision tree construction” In ACM SIGMOD Record, vol. 28, pp. 169–180, 1999.

N. Sanchez-Marono, A. Alonso-Betanzos, M. Tombilla-Sanroman, “Filter methods for feature selection - a comparative study”, Intelligent Data Engineering and Automated Learning-IDEAL, pp. 178-187, 2007.

N. Sengupta, J. Sen, J. Sil, M. Saha, “Designing of on line intrusion detection system using rough set theory and Q-learning algorithm”, Neurocomputing, vol. 111, pp. 161-168, 2013.

http://www.cs.waikato.ac.nz/ml/weka/, [Online] access 2nd August 2017.

O. Osanaiye, R. Choo, M. Dlodlo, “Analysing feature selection and classification techniques for DDoS detection in cloud”, In Proceedings of the Southern Africa Telecommunication, pp. 198-203, 2016.


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