Dynamic Malware Analysis and Node Behavior Monitoring with Self-Replication for Enhanced Network Intrusion Detection

Cyber-attacks target another individual or organization’s technological infrastructure. When several activities undermine the accessibility, privacy, or reliability of a computer system resource, a compromise is detected. Intrusion Detection Systems warn management stations when they identify unusual network or system activities, such as security policy violations. Cyber networks send too much data for real-time detection of threats. The project focuses on detecting cyber threats using artificial neural networks. Checking user behavior for abnormalities may help spot fraud. How systems can detect new vulnerabilities and respond to mitigate or eliminate threats is a major cyber security topic. Detecting unfriendly nodes before they can interact prevents cyber assaults, requiring sophisticated safety measures. A self- replication technique for spotting network breaches using dynamically intrusion pattern identification and quick node activity analysis is proposed in this paper. This paradigm reduces false positives in highly dynamic systems with frequent behavior changes. Dynamic malware analysis and node behavior may properly identify malware. Alerts given when anything risky happens allow instantaneous delivery of solutions to other systems and avoidance of assaults on various systems. The self-healing technology protects the network against malicious applications. The healing themselves model protects the system from infections by malware and information loss with $98 \%$ accuracy, while the proposed model analyzes patterns dynamically with $\mathbf{9 8. 4 \%}$ accuracy.