Enhancing Data Security: Machine Learning Approaches for Intrusion Detection in Computer Networks


  • Diptiben Ghelani Department of Computer Engineering, Gujarat Technological University, India


Machine Learning, Intrusion Detection, Data Security, Computer Networks, Cyber Threats, Deep Learning


Abstract: Ensuring the security of computer networks against cyber threats is paramount in today's interconnected world. This paper explores the application of machine learning (ML) approaches for enhancing data security through the detection of intrusions in computer networks. By leveraging the vast amount of network traffic data generated in modern computing environments, ML algorithms can effectively identify and mitigate various types of cyber attacks in real-time. This paper provides a comprehensive overview of ML-based intrusion detection systems (IDSs), including their design principles, key components, and evaluation metrics. Furthermore, it discusses the challenges and limitations associated with ML-based IDSs, such as the need for labeled training data, model interpretability, and adaptability to evolving threats. The paper also examines recent advancements in ML techniques, such as deep learning and reinforcement learning, and their potential applications in intrusion detection. Through a critical analysis of existing research literature and case studies, the paper highlights the strengths and weaknesses of different ML approaches for intrusion detection, offering insights into their effectiveness, scalability, and practicality in real-world deployment. Additionally, it discusses the implications of ML-based IDSs for network security operations, including their integration with existing security infrastructure, deployment considerations, and operational challenges. Finally, the paper outlines future research directions and recommendations for advancing the state-of-the-art in ML-based intrusion detection, including the development of hybrid approaches, automated model selection techniques, and robust evaluation methodologies. Overall, this paper contributes to the growing body of knowledge on ML applications in data security and provides guidance for researchers, practitioners, and policymakers seeking to deploy effective intrusion detection systems in computer networks.