Malware Seeker: A Network Intrusion Detection and Correlation Technique against Peer to Peer Botnet

Current research has been carried out against Malware propagating in the Peer to Peer parallel distributed system is challenging and cumbersome task. In Existing solutions, lot of efforts has been carried against the malware evolution and activities but solutions are ineffective against the detection rate and accuracy in detection due to growing of high traffic calls to the networks. In this paper, we propose a novel Solution to mitigate the malicious activities of peer to peer Botnet attackers through the detection mechanism and countermeasure strategies named as Malware Seeker. To prevent vulnerable Host machines from being compromised by the peer to peer Malware, we propose a multiphase distributed vulnerability detection through the Principle of component analysis of each traffic data, measurement and countermeasure selection mechanism called Malware Seeker which is built on attack graph-based analytical models based on classification process and reconfigurable against update solutions to virtual network-based countermeasures with respect to command and Control established by botmaster. The proposed framework leverages hierarchical models to build a monitor and control process to classify the network traffic data to the virtual machine to significantly improve attack detection and mitigate attack consequences such as spamming, scanning an exploitation. Extensive Evaluation will demonstrate the behaviors of the proposed System against the Malware in file sharing process with legitimate and illegitimate and Malware causes in the peer to peer network process with huge amount of network information.