A Comprehensive Review of Cybersecurity Threats and Mitigation Strategies

Abstract : Cybersecurity threats continue to evolve in sophistication and scale, targeting organizations and individuals across all sectors. This expanded review surveys major threat categories — malware, phishing and social engineering, ransomware, denial-of-service attacks, insider threats — and examines emerging trends (AI, blockchain, quantum, IoT). For each category, we discuss detailed subtypes and attack methods, detection and defense techniques, and relevant case examples, drawing on the latest research. Notably, recent studies underscore that advanced machine learning and deep learning models have significantly improved malware detection and classification accuracy, while novel social engineering attacks exploit generative AI to craft highly convincing phishing content. The economic dimension of ransomware is examined through empirical analyses, showing how backups, insurance, and data exfiltration influence ransom payments. We also review the unprecedented rise in DDoS attack volume and discuss next-generation mitigation (AI-based anomaly detection, cloud scrubbing, anycast). Insider threats are analyzed in terms of motivations (malicious or negligent) and advanced detection (behavioral analytics). Finally, emerging trends such as AI-driven security (offensive and defensive), blockchain-based integrity solutions, the NIST post-quantum cryptography initiative, and IoT-specific vulnerabilities are addressed. This comprehensive review integrates recent findings from academic and government sources to inform practitioners and researchers about current threats and robust mitigation strategies.