Hybrid Intrusion Detection System Development in Wireless Sensor Networks

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 162 - 174, 20.10.2021

Hamza Elbahadır , Ebubekir Erdem

https://doi.org/10.53070/bbd.990934

Abstract

Since wireless sensor networks (WSNs), which have widespread usage areas today, are different from traditional network architecture, security solutions specific to WSNs should be produced. In this study, an intrusion detection system (IDS) is proposed for WSN security. For an effective security, a hybrid model including anomaly and misuse-based detection methods used in intrusion detection system has been studied. Data mining algorithms BayesNet, J48, JRip, PART and RandomForest were used to classify the normal and attack traffic of the system and the performance values of the algorithms are shared. In this study, CSE-CIC-IDS2018, an up-to-date data set, was used, unlike the existing studies in the literature. Considering the WSN performance criteria, the data in the data set was preprocessed. The results showed that the proposed system has high accuracy.

Keywords

Wireless sensor networks, intrusion detection, anomaly, hybrid

Kablosuz Algılayıcı Ağlarda Hibrit Saldırı Tespit Sistemi Geliştirme

Abstract

Günümüzde yaygın kullanım alanlarına sahip olan kablosuz algılayıcı ağlar (KAA), geleneksel ağ mimarisinden farklı olduğundan, özgün güvenlik çözümleri üretilmelidir. Bu çalışmada KAA güvenliği için saldırı tespit sistemi (STS) önerilmiştir. Etkili bir güvenlik için, saldırı tespit sistemlerinde kullanılan anomali ve yanlış kullanım tabanlı algılama metotlarını ihtiva eden hibrit bir model üzerinde çalışılmıştır. Sistemin normal ve saldırı trafiğini sınıflandırabilmesi için veri madenciliği algoritmalarından BayesNet, J48, JRip, PART ve RandomForest algoritmaları kullanılmış ve söz konusu algoritmaların performans değerleri paylaşılmıştır. Bu çalışmada literatürdeki mevcut çalışmalardan farklı olarak, güncel bir veri seti olan CSE-CIC-IDS2018 kullanılmıştır. Veri setindeki veriler ise, KAA performans kriterleri göz önünde bulundurularak ön işleme tabi tutulmuştur. Sonuçlar önerilen sistemin yüksek doğruluk oranına sahip olduğunu göstermiştir.

Keywords

Kablosuz algılayıcı ağ, saldırı tespit, veri madenciliği, hibrit

References

• Kumar, V., Jain, A., Barwal, P. N. (2014) “Wireless Sensor Networks: Security Issues, Challenges and Solutions”, International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 8, pp. 859-868.
• Chelli, K. (2015) “Security Issues in Wireless Sensor Networks: Attacks and Countermeasures”, Proceedings of the World Congress on Engineering Vol I
• Deng, R., Zhuang, P., Liang, H. (2017) “CCPA: Coordinated Cyber-Physical Attacks and Countermeasures in Smart Grid,” IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2420–2430
• Padmavathi, G., Shanmugapriya, D. “A Survey of Attacks, Security Mechanisms and Challenges in Wireless Sensor Networks”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 4, No. 1 & 2
• Tomić I., McCann, J. A. (2017) "A Survey of Potential Security Issues in Existing Wireless Sensor Network Protocols", in IEEE Internet of Things Journal, vol. 4, no. 6, pp. 1910-1923, doi: 10.1109/JIOT.2017.2749883.
• Ozcelik, M., Irmak, E., Ozdemir, S. (2017) "A Hybrid Trust Based Intrusion Detection System for Wireless Sensor Networks", International Symposium on Networks, Computers and Communications, Marrakech, pp. 1-6
• Ghugar, U., Pradhan, J., Bhoi, S. K., Sahoo, R. R. (2019) “LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System”, Journal of Computer Networks and Communications Volume, Article ID 2054298
• Çavuşoğlu, Ü., Kaçar, S. (2019) "Anormal Trafik Tespiti için Veri Madenciliği Algoritmalarının Performans Analizi", Academic Platform Journal of Engineering and Science 7-2, 205-216
• Acharya, N., Singh, S. (2018) "An IWD-based feature selection method for intrusion detection system", Soft Comput 22, 4407–4416
• Altun, B. (2016) "Kablosuz Sensör Ağları ve Uygulama Alanları", Karabük Üniversitesi Mühendislik Fakültesi, 61-62
• Martins, D., Guyennet, H. (2010) "Wireless Sensor Network Attacks and Security Mechanisms: A Short Survey," 13th International Conference on Network-Based Information Systems, pp. 313-320,
• Dolay, B. (2009) "Kablosuz Sensör Ağları", https://e-bergi.com/y/kablosuz-sensor-aglari, Son Erişim: 3 Eylül 2021
• Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., Cayirci, E. (2002) “Wireless Sensor Networks: A Survey”. Computer Networks, 38. 393-422. 10.1016/S1389-1286(01)00302-4.
• Amara, S., Beghdad, R., Oussalah, M. (2013). "Securing Wireless Sensor Networks: A Survey." EDPACS. 47. 10.1080/07366981.2013.754207.
• Dewal P., Narula G.S., Jain V., Baliyan A. (2018) Security Attacks in Wireless Sensor Networks: A Survey. In: Bokhari M., Agrawal N., Saini D. (eds) Cyber Security. Advances in Intelligent Systems and Computing, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-8536-9_6
• Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion Detection Systems in Wireless Sensor Networks: A Review. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2013/167575
• Farooqi, A., Khan, F. (2009). "Intrusion Detection Systems for Wireless Sensor Networks: A Survey." International Journal of Ad Hoc and Ubiquitous Computing. 9. 234-241. 10.1504/IJAHUC.2012.045549.
• Ozgur, D., Topallar, M., Anarim, E., Ciliz, M.K. (2005). An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl.. 29. 713-722. 10.1016/j.eswa.2005.05.002.
• Silva, A. P., Martins, M., Rocha, B., Loureiro, A., Wong, H.(2005) "Decentralized intrusion detection in wireless sensor networks." 16-23. 10.1145/1089761.1089765.
• Canadian Institute for Cybersecurity. https://www.unb.ca/cic/datasets/ids-2018.html, Son Erişim: 3 Eylül 2021
• Information theory, Wikipedia https://en.wikipedia.org/wiki/Information_theory, Son Erişim: 3 Eylül 2021
• Bayes Ağı, Wikipedia, https://tr.wikipedia.org/wiki/Bayes_a%C4%9F%C4%B1, Son Erişim: 3 Eylül 2021
• Data Mining Algorithms In R/Classification/JRip, https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/JRip, Son Erişim: 3 Eylül 2021
• Sonawani, S., Mukhopadhyay, D. (2013) "A Decision Tree Approach to Classify Web Services using Quality Parameters”
• Ali, S., Smith, K. "On learning algorithm selection for classification." Applied Soft Computing. 6. 119-138. 10.1016/j.asoc.2004.12.002
• Liu Y., Wang Y., Zhang J. (2012) New Machine Learning Algorithm: Random Forest. In: Liu B., Ma M., Chang J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_32