Cyber Attack Prediction: From Traditional Machine Learning to Generative Artificial Intelligence

The escalating sophistication of cyber threats poses significant risks to individuals, organizations, and nations. Cybercrime, encompassing activities like hacking and data breaches, has severe economic and societal consequences. In today's interconnected world, robust cybersecurity measures are paramount to mitigate these risks and protect sensitive information. However, traditional security solutions struggle to keep pace with the evolving threat landscape. Artificial Intelligence (AI) offers a powerful arsenal of techniques to address these challenges. This paper explores the application of AI methods, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Explainable AI (XAI), and Generative AI, in solving various cybersecurity problems. This paper presents a comprehensive analysis of AI techniques for enhancing cybersecurity. Key contributions include: 1) comparative study of ML and DL methods: Evaluating their accuracy, applicability, and suitability for various cybersecurity challenges; 2) investigation into XAI approaches: Enhancing the transparency and interpretability of AI-powered security solutions, particularly in anomaly detection; 3) exploration of emerging trends in Generative AI (Gen-AI) and NLP: Examining their potential to simulate and mitigate cyber threats through advanced techniques like threat intelligence generation and attack simulations; 4) application of GenAI in cybersecurity and real-world products of GenAI for cyber security. This research aims to advance the state-of-the-art in AI-driven cybersecurity by providing insights into effective and reliable solutions for mitigating cyber risks and improving the overall security posture.