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.