Utilisation of Artificial Intelligence and Cybersecurity Capabilities: A Symbiotic Relationship for Enhanced Security and Applicability

The increasing interconnectivity between physical and cyber-systems has led to more vulnerabilities and cyberattacks. Traditional preventive and detective measures are no longer adequate to defend against adversaries. Artificial Intelligence (AI) is used to solve complex problems, including those of cybersecurity. Adversaries also utilise AI for sophisticated and stealth attacks. This study aims to address this problem by exploring the symbiotic relationship of AI and cybersecurity to develop a new, adaptive strategic approach to defend against cyberattacks and improve global security. This paper explores different disciplines to solve security problems in real-world contexts, such as the challenges of scalability and speed in threat detection. It develops an algorithm and a detective predictive model for a Malicious Alert Detection System (MADS) that is an integration of adaptive learning and a neighbourhood-based voting alert detection framework. It evaluates the model’s performance and efficiency among different machines. The paper discusses Machine Learning (ML) and Deep Learning (DL) techniques, their applicability in cybersecurity, and the limitations of using AI. Additionally, it discusses issues, risks, vulnerabilities, and attacks against AI systems. It concludes by providing recommendations on security for AI and AI for security, paving the way for future research on enhancing AI-based systems and mitigating their risks.