Dynamic Malware Pattern Analysis with Rapid Node Behaviour Analysis Using Self Replication Model for Network Intrusion Detection

Advanced machine learning and artificial intelligence-based malware identification and categorization activities in real time are the primary emphasis of Malware Analysis and Intrusion identification in Cyber Physical Systems, along with the time sequence output of observed activity.Malware and other cyber threats have prompted the development of numerous static and behavior based detection approaches.These cyber security solutions show promise on large datasets, but they aren't reliable or resilient enough for real-world detection.Problems like virus detection and the identification of malevolent behavior highlight the critical need for improved cyber security solutions based on artificial intelligence.For those who utilize the internet, malware has become an enormous issue.The application is executed in a secure virtual environment and its actions are tracked in real-time to facilitate dynamic malware detection.A lot of people utilize API sequence analysis to find out if the software that is currently running is dangerous.While existing systems do consider API names and usage frequencies, feature mining of API sequence falls short, making it possible for some malware to evade detection.The two mainstays of dynamic analysis now in use either modify the virus itself or use an elevated component to execute the analysis.In contrast to the latter, which usually causes a discernible performance overhead, the former is instantly identifiable by even the most sophisticated malware.One of the most important steps in avoiding cyber assaults is developing new cyber security methods to detect hostile nodes before they communicate.Traditional dynamic malware detection models need to monitor the nodes more keenly for deep pattern analysis and eradicating nodes that cause malicious actions in the network.This research proposes a Dynamic Malware Pattern Analysis with Rapid Node Behaviour Analysis using Self Replication Model (DMPA-RNBA-SRM) for Network Intrusion Detection in the network.The normal patterns will be allowed into the network and the patterns of the nodes that are unusual are not allowed temporarily.The Pattern analysis and updating is performed and the detected patterns are analyzed and if they are malicious in nature, they will not be allowed into the network.The self replication model will be triggered when a unusual pattern is detected and required actions are performed in the network.The proposed model dynamic pattern analysis and detection is high when compared to traditional models.