Protecting and Mitigating Against Cyber Threats

1Foundations of AI and ML in Security

Sunil Kumar Mohapatra^*, Ankita Biswal, Harapriya Senapati, Adyasha Swain and Swarupa Pattanaik

Centurion University of Technology and Management, Bhubaneswar, India

Abstract

The Internet has been ingrained in people’s daily lives worldwide; simultaneously, online criminal behavior has inspired advances in cybersecurity. Traditional cybersecurity approaches involve proactive efforts involving technologies, best practices, and policies to ensure information confidentiality, integrity, and availability. However, they have limitations, such as relying on static defense mechanisms, struggling with advanced threats, being dependent on perimeter defense, and the false positives/negatives. These vulnerabilities lead to increased Phishing attacks, Ransomware attacks, DDoS, MitM attacks, SQL Injection, IOT exploitation, and Social Engineering attacks. So, several data-driven computational models such as AI and ML have been revolutionized to address these security issues. The pillar of AI and ML in security lies in their potential to inspect vast amounts of data, make predictions, and detect patterns or decisions without explicit programming. Feature engineering methodology selects, manipulates, and builds essential features from raw data to improve the effectiveness of machine learning models in detecting and preventing cyber-attacks. These processes contribute to developing a clean, informative, and balanced dataset to train accurate and trustworthy ...