Protecting and Mitigating Against Cyber Threats

4Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS)

Aryan Kapoor¹, Jayasankar K.S.¹, Pranay Jiljith¹, Abishi Chowdhury¹, Shruti Mishra^2*, Sandeep Kumar Satapathy², Janjhyam Venkata Naga Ramesh³ and Sachi Nandan Mohanty⁴

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

²Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, Tamil Nadu, India

³Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., Andhra Pradesh, India

⁴School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India

Abstract

Organizations now handle cyber risks differently as a result of the cybersecurity field’s application of artificial intelligence (AI) and machine learning (ML). Using these technologies to strengthen cybersecurity postures has become essential as the frequency and sophistication of cyberattacks rise. AI and ML are effective weapons in the battle against cybercrime because they can process enormous amounts of data and recognize different kinds of threats, such as malware and phishing attempts. This paper offers a comparative study evaluating different machine learning models integrated with a Network Intrusion Detection System (NIDS). The aim of the study is to evaluate the performance and effectiveness of several machine learning algorithms for ...