CASCADED HYBRID ARCHITECTURE FOR NETWORK ANOMALY DETECTION BASED ON ISOLATION FOREST AND GAN AUTOENCODERS
The research is dedicated to improving the security of telecommunication networks by developing a hybrid system for detecting anomalies in traffic. The problem of the work is due to the inefficiency of traditional signature-based Intrusion Detection Systems (IDS) against complex, time-distributed cyberattacks. A two-level architecture is proposed, which aggregates statistical and deep behavioral machine learning through an adaptive Decision Fusion module.