INTRUSION DETECTION SYSTEM USING MACHINE LEARNING ALGORITHMS

Abstract

Wireless Sensor Networks (WSNs) are increasingly deployed in critical applications such as environmental monitoring, healthcare, and smart infrastructure. However, their open communication medium and limited computational resources make them highly vulnerable to various cyberattacks. To address these challenges, this work presents an Intrusion Detection System (IDS) based on machine learning algorithms for accurate and efficient attack detection in WSN environments. The proposed model performs systematic data preprocessing, feature selection, and classification using multiple learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, and Deep Neural Network models. These algorithms are trained and evaluated on benchmark network intrusion datasets to detect both normal and malicious traffic. Experimental results demonstrate that the proposed IDS achieves high detection accuracy, low false-alarm rates, and improved computational efficiency compared to traditional signature-based approaches. The study confirms that machine learning techniques can effectively enhance the resilience and adaptability of intrusion detection in wireless sensor networks.