Smart Surveillance

This book explores the growing global threat of cyber-attacks and cyber-crime highlighting the financial and societal impacts they cause. It presents a novel approach to predicting and identifying cyber-attacks and their perpetrators by leveraging real-world data such as crime type perpetrator gender damage assessment and attack methods. Drawing from victim-reported incidents and forensic investigations the study utilizes two machine learning models to analyze cyber-crime patterns. The book also evaluates the effectiveness of different features in improving the accuracy of cyber-attack detection and perpetrator identification offering valuable insights into AI-driven cybersecurity solutions.