Modeling Intrusion Detection System Using Machine Learning Algorithms in Wireless Sensor Networks - Elbahadir, Hamza; Erdem, Ebubekir (Institute of Electrical and Electronics Engineers Inc., 2021)
Wireless sensor networks (WSN) are used to perceive many data such as temperature, vibration, pressure in the environment and to produce results; it is widely used, including in critical fields such as military, intelligence and health. However, because of WSNs have different infrastructure and architecture than traditional networks, different security measures must be taken. In this study, an intrusion detection system (IDS) is modeled to ensure WSN security. Since the signature, misuse and anomaly based detection methods for intrusion detection systems are insufficient to provide security alone, a hybrid model is proposed in which these methods are used together. In the hybrid model, anomaly rules were defined for attack detection, and machine learning algorithms BayesNet, J48 and Random Forest were used to classify normal and abnormal traffic. Unlike the studies in the literature, C S E - C I C - IDS2018, the most up-to-date data set, was used to create attack profiles. Considering both hardware constraints and battery capacities of WSNs; the data was pre-processed in accordance with data mining principles. The results showed that the developed model has high accuracy and low false alarm rate. © 2021 IEEE
Kablosuz Algilayici Aglarda Makine Ögrenmesi Algoritmalari Kullanarak Saldiri Tespit Sistemi Modellenmesi
6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 176826
Keywords: Hybrid system; Intrusion detection system; Machine learning; Wireless sensor networks