V2I2P5

AI-Powered Cybersecurity: Threat Detection and Mitigation

Arpit Harsh^1*, Durgesh Kumar Jha¹, Anant Samrat¹, Mayank ¹, Akash Kumar¹

Abstract

It is impossible to overestimate the significance of cyber security in a time when electronic communications become a part of everyday life. This study explores the crucial field of cyber security, paying particular attention to network security and the necessity of efficient threat detection. Traditional approaches are frequently ineffective because to the increasing frequency and complexity of cyberthreats. In order to improve threat detection skills, this research investigation investigates the creative use of machine learning methods. We start by looking over the body of research on cyberthreats and the shortcomings of traditional detection techniques. We next suggest a system for real-time threat identification and mitigation that makes use of machine learning methods, including supervised and unsupervised learning techniques. By training several models on a variety of network traffic datasets, our methodology successfully allowed the algorithms to learn and adjust to new threats. When compared to conventional systems, the results show a notable improvement in reaction time and detection accuracy. Interestingly, our machine learning models demonstrated low rates of false positives and a high true positive rate, highlighting their dependability in detecting real threats without bombarding security staff with false alerts. To sum up, this study demonstrates how machine learning can revolutionize the detection of threats to cyber security. Businesses may strengthen their defences against cyberattacks, protect sensitive data, and uphold stakeholder trust by incorporating these cutting-edge strategies into their current security frameworks. This report encourages greater advancements in this crucial area and lays the foundation for a more secure digital world.

Keywords: Cyber Security, Network Security, Threat Detection, Machine Learning