An Incremental Majority Voting Approach for Intrusion Detection System Based on Machine Learning

With the rapid growth of digitalization and the increasing volume of data, the cybersecurity threat landscape is expanding at an alarming rate. Intrusion Detection Systems (IDS) have been widely employed in conjunction with firewalls to safeguard networks. However, traditional IDS systems operate in a static manner, rendering them vulnerable to obsolescence and necessitating costly retraining efforts. As a result, the demand for dynamic models capable of handling continuous streams of network traffic has surged as they can learn from the incoming traffic without the need to old data and costly retraining. In response to this challenge, we have implemented an enhanced approach: an incremental majority voting IDS system, which utilizes existing tools and techniques to improve the robustness and adaptability of intrusion detection By leveraging the collective decision-making power of multiple machine learning models such as: KNN classifier, Softmax Regressor and Adaptive Random Forest classifier, our system aims to improve the accuracy, especially reducing false alarm rates, and effectiveness of intrusion detection in real-time scenarios. Through this research, we have managed to obtain a model which scores 96.43% of accuracy as well as giving 100% precision for majority type of attacks. By successfully handling the imbalanced nature of streaming data, our adopted model shows promising potential as a high-performing solution for IDS and can be considered one of the robust IDS models capable of dealing with real-world imbalanced datasets.