Network Hazard Flow for Multi-Tiered Discriminator Analysis Enhancement with Systems-Theoretic Process Analysis

Network hazard flow and malware protection is a point of concern for many large organizations and there is a problem of insufficient readiness of the used protection tools against information threat. Disaster preparedness for autonomous infrastructure is complex and embedded with unknown Risks. The conditions of hazard flow and malware protection are even worse in small and medium enterprises, educational institutes, and governmental networks. The present study is based on a trade-off analysis of hazards and malware protection by using a modified version of the System Theoretical Process Analysis (STPA) methodology. The study is aimed to highlight the analysis of hazards, use of STPA features for hazard analysis, and possible alternate methodologies for hazard analysis linked to botnet intrusions. Modern malware and security threats are strong enough to overcome the traditional anti-virus software. The novel model is a hybrid of prediction Generative Adversarial Network (GAN) and cyclic ingestion with unique monitoring engine features for enhancing the identification, system resiliency, and overall performance. Performance evaluation shows that the proposed approach results in better detection over the state-of-the-art botnet detection.