Self-Replicating Model for Efficient Malware Pattern and Node Behavior Analysis in Network Intrusion Detection

The DMPA-RNBA-SRM intrusion detection system is shown in this suggested research. Unfortunately, static analysis approaches aren't very good at spotting camouflaged polymorphic malware, and the quantity and sophistication of malware threats are only becoming worse. By keeping tabs on analysis via node activities and communicational trends inside a network, this model employs dynamic pattern analysis. It then notifies the security system of any unforeseen events. Once the system identifies potentially dangerous stations, it initiates the self-copy protection program, which disables the offending nodes and alerts the network administrators to minimise the damage. The results show that DMPA-RNBA-SRM is quite accurate; it achieves 99.5% success rate in malware detection and 99.6% success rate in self-healing procedures. Consequently, this proactive, self-healing method maximises efficiency by minimising the number of false positives, adjusting to new threats, and drastically cutting down on the need for human intervention in network security. Improving the scalability and identification quality in large size and complicated networks is the goal of future work that builds on the presented model using hybrid optimisation methods and deep neural networks. Therefore, the present cyber defence mechanisms are greatly enhanced by this model's accuracy as a malware identification tool and its safeguarding of all nodes.