ABSTRACT
The cybersecurity landscape is undergoing a significant transformation, driven by the escalating sophistication and prevalence of cyber threats. Traditional defensive measures, focused on detection and prevention, are no longer sufficient to combat these evolving threats. To address this challenge, a more comprehensive strategy is required, incorporating advanced cybersecurity measures such as artificial intelligence (AI)-powered attack modelling and simulation tools. This review paper presents a thorough examination of existing attack simulators, analysing their capabilities, limitations, and applications in AI-driven cybersecurity. Our systematic review of 25 attack simulators reveals a predominant focus on network-based attacks, with limited coverage of endpoint, cloud, or IoT attacks. AI-powered simulators demonstrate enhanced accuracy and efficiency in predicting attack vectors and identifying vulnerabilities. However, current simulators lack realistic user behaviour modelling, hindering the accuracy of attack simulations. Furthermore, integration with existing security tools and frameworks is crucial for effective attack simulation and remediation.Our review highlights the need for holistic attack simulators addressing diverse attack types and vectors, enhanced AI-powered simulation capabilities for improved accuracy and efficiency, realistic user behaviour modelling to simulate real-world attack scenarios, and standardized evaluation metrics for attack simulators to ensure comparability and effectiveness. This review serves as a foundation for future research in AI-powered attack modelling and simulation, providing valuable insights for cybersecurity professionals seeking to enhance their defences against evolving cyber threats, researchers exploring innovative approaches to cybersecurity, and organizations aiming to bolster their cybersecurity posture.
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