Exploiting Machine Learning for Robust Security
Tarun Kumar, Minakshi Memoria, Shree Harsh Attri · 2025
The chapter discusses cybercrime's significant global economic impact, resulting in a 0.9% GDP loss and approximately $600 billion in damages in 2017. It outlines the evolving cybersecurity threat landscape, focusing on dangerous types like malicious software, automated attacks, and advanced persistent threats that challenge traditional security measures. The chapter highlights artificial intelligence and machine learning's emerging roles in cybersecurity, showing how these technologies enhance security automation, response times, and risk assessment. Deep learning shows particular promise in real-time processing and classification, offering solutions for malware and social engineering detection. The chapter emphasizes the need for ongoing research while noting challenges in integrating machine learning into cybersecurity practices. Additionally, it underscores the historic scientific value of intelligent cybersecurity advancement and the importance of establishing clear research milestones.