AI-Driven cyber threats: unraveling deepfakes, autonomous malware, and defensive strategies

Abstract

Cyber threats are now more complicated and harder than ever with the swift development of artificial intelligence (AI). This study focuses on three key aspects of AI-driven cyber threats: social engineering with fake personal media, self modifying malware that attacks automatically and the tough challenges in defending systems against AI-backed cyberweapons. Because deepfake technology uses generative AI, it can make it simple and very convincing for criminals to carry out phishing and deceitful impersonation attacks on humans. Because it is driven by AI, autonomous malware can quickly transform to bypass usual anti-malware systems. Cyberweapons that incorporate AI such as automated creation of vulnerabilities, are a huge threat to computer networks. A comprehensive review of literature, as well as proposing a mixed methodology, this study studies how these threats work, their effects and the ways to control them. The results suggest that AI-based attacks are becoming more hidden and can be used on a large scale, so new defense methods are required. Participants cover the effectiveness of AI in detecting hackers, certain problems with today’s cybersecurity methods and what is and is not ethical when using AI in cybersecurity. To address these developing hazards, this paper proposes a multi-layered defense system that uses machine learning, deep learning and metaheuristic algorithms. The study urges the use of quick, flexible and ethical cybersecurity methods to defend vital systems in a world dominated by AI. This study supports current ongoing conversations by providing new findings for researchers, policymakers cybersecurity.