Data Science and Intelligent Computing Techniques
Machine Learning Approaches Intrusion for Improving Network-Based Detection System
Authors: Seethal Prince E and Mahesh K M
Publishing Date: 13-12-2023
ISBN: 978-81-955020-2-8
Abstract
The field of network security has gained paramount importance in response to the ever-growing advancements in internet and communication technologies. With the aim of safeguarding the integrity of networks and their components in the digital realm, a suite of tools including firewalls, antivirus software, and intrusion detection systems (IDS) has been deployed. Among these, network-based intrusion detection systems (NIDS) hold a pivotal role by continuously monitoring network traffic for any signs of malicious or suspicious activities. However, the relentless pace of technological progress in the past decade has led to the expansion of larger, more complex networks supporting a multitude of applications, thereby creating significant challenges in maintaining data and network node security. The existing IDSs have revealed their limitations in detecting various forms of attacks, including zero-day attacks, and mitigating false alarm rates (FAR). Consequently, the demand for cost-effective, precise, and efficient NIDS solutions is on the rise to fortify network security
Keywords
Network Security, IDS, NIDS, Network Node Security, Zero-Day Attacks, False Alarm Rates
Cite as
Seethal Prince E and Mahesh K M, "Machine Learning Approaches Intrusion for Improving Network-Based Detection System", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 765-780. https://doi.org/10.56155/978-81-955020-2-8-66