Can AI Outsmart Cybercriminals? A Systematic Review of AI-Driven Cyber Defense
Abstract
Cybercrime has become a critical challenge in the digital age, posing significant threats to individuals, businesses, and global infrastructures. As cybercriminals leverage sophisticated techniques, traditional cybersecurity measures often struggle to keep pace. Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity, offering automated, adaptive, and intelligent solutions for threat detection, incident response, and fraud prevention. This systematic literature review (SLR) synthesizes findings from 47 peer-reviewed studies to examine AI’s role in cyber defense. The review explores machine learning (ML), deep learning (DL), blockchain security, adversarial AI, and explainable AI (XAI) in mitigating cyber threats, including malware, phishing, ransomware, and financial fraud. Findings indicate that AI-driven threat detection systems significantly improve accuracy, with models achieving over 99% precision in malware and fraud detection. AI-powered forensic tools enhance cybercrime investigations, while deep reinforcement learning (DRL) and user behavior analytics bolster proactive cybersecurity measures. However, challenges remain, including algorithmic bias, adversarial attacks, ethical concerns, and regulatory gaps. The study underscores the need for transparent AI policies, interdisciplinary cybersecurity strategies, and global cooperation to ensure responsible AI deployment. This review provides key insights for researchers, policymakers, and cybersecurity professionals by identifying emerging trends, limitations, and future research directions. It emphasizes the necessity of adaptive, ethical, and explainable AI frameworks to address evolving cyber threats and fortify digital security in an increasingly interconnected world.
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