MACHINE LEARNING-BASED IDS: ANALYZING EFFECTIVENESS, SCALABILITY, AND ADAPTABILITY IN NETWORK SECURITY.

Abstract

In the present era of digital transformation, keeping systems secure from cyber threats is a global concern. The traditional Intrusion Detection System, which relies on predefined signatures, falls short and stands no chance against sophisticated, morphing threats. This research work tries to benchmark the performance of machine learning models in enhancing the capabilities of IDS systems with respect to the latter's effective, scalable, and adaptive performance across various network environments. Two models—a Logistic Regression model and a Random Forest model—were developed and evaluated using a comprehensive dataset of network traffic. The Random Forest model significantly outperformed the Logistic Regression model, nearly reaching 100% accuracy. These findings give insight into possibilities where machine learning may take place for the development of robust, adaptive IDS systems and, thus, call for more research on issues of balance related to accuracy, efficiency, and interpretability. This work also points toward issues regarding data set diversity and robustness in crafting appropriate IDS solutions.