Anomaly-Based Intrusion Detection System in Wireless Sensor Networks using Machine and Deep Learning Algorithms

Abstract

One of the most significant issues in Wireless Sensor Networks (WSNs) is security, which must address to keep WSNs safe from malicious attacks. An Intrusion Detection System (IDS) is essential in analyzing network traffic and detecting abnormal events. However, these IDSs suffer from several drawbacks that affect their effectiveness and flexibility in accuracy, so they must overcome these to improve IDS performance. These drawbacks include difficulties in determining the appropriate dataset, the problem of feature selection, the issue of the imbalanced dataset, and choosing suitable algorithms for the classification process in WSN. This research proposed a model for an anomaly-based IDS in WSNs. This model applied mutual information (MI) for feature selection and the synthetic minority oversampling technique (SMOTE) to solve the imbalanced dataset problem. It used different machine learning (ML) (random forest (RF), decision tree (DT), support vector machine (SVM), and k nearest neighbors (KNN)) and deep learning (DL) deep neural network (DNN) algorithms to analyze network traffic and classification binary or multi-classification. To implement and measure the performance of the proposed model, relying on the standard dataset NSL-KDD. Python language is used in the Anaconda platform, and many metrics to evaluate and measure the performance of the proposed model. Experimental results show that the proposed model can accurately detect intrusion using ML and DL algorithms. The results of the proposed model for different algorithms achieved better performance than the state-of-the-art, the maximum enhancement reached 15%.