MATLAB-Based Probabilistic Forecasting of AI-Assisted Cyberattacks Against Web Applications

The increasing dependence on web applications and internet-based services has created a growing need for proactive cybersecurity approaches. As cyber threats continue to evolve, conventional security mechanisms primarily focused on intrusion detection and incident response may not provide sufficient preparation time before an attack occurs. Furthermore, advancements in artificial intelligence (AI) have introduced new possibilities for automating cybersecurity-related activities, including reconnaissance, vulnerability analysis, and attack orchestration. These developments raise questions about how AI-assisted attacker activities may influence the cybersecurity threat landscape. This research proposes the development of a MATLAB-based probabilistic forecasting framework designed to estimate the likelihood of future cyberattacks against web applications. The primary objective is to investigate whether historical cybersecurity information, temporal attack patterns, and relevant security indicators can be utilized to estimate the probability of a cyberattack occurring within a predefined future time interval.