HYBRIDMODELFOR DETECTING CYBERSECURITY THREATS BASED ON DEEP LEARNING WITH AN OPTIMISATION ALGORITHM

Digital transformation and the Fourth Industrial Revolution have inevitably led to the emergence of new cybersecurity threats. Protection against these attacks is critical for individuals, businesses, organisations and countries as a whole. Effective threat detection depends on identifying both known and unknown risks and vulnerabilities as early as possible through a combination of visibility,analytics, and contextual awareness.Traditional risk assessment methods, in particular deterministic approaches to threat analysis, often fail to take into account the high level of uncertainty and variability in operating conditions. This article proposes an intelligent hybrid system for detecting cybersecurity threats based on a deep neo-fuzzy neural network with a combined optimisation algorithm for detecting and preventing relevant attacks.