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Classification and Adaptive Novel Class of Botnet Detection

malicious software typically resides stealthily on a user’s computer and interacts with the user’s computing resources. Our goal in this work is to improve the trustworthiness of a host and its system data. By providing a new mechanism that ensures the correct origin or provenance of critical system information and prevents adversaries from utilizing host resources. It defines the data provenance integrity as the security property stating that the source data is generated which cannot be spoofed or tampered with the host resources. The decentralized nature of Peer-to-Peer (P2P) botnets makes them difficult to detect, their distributed nature also exhibits resilience against take-down attempts, and moreover smarter bots are stealthy in their communication patterns and elude the standard discovery techniques which look for anomalous network or communication behaviour. In this project, a novel methodology is used to detect P2P botnet traffic and differentiate it from P2P traffic in a network. A cryptographic protocol for ensuring secure and timely availability of the reputation data of a peer to other peers at extremely low costs. Content auditing is done on receiver side even signature fails and mentions the ratio or impact of fake content. As result, a peer’s reputation motivates it to cooperate and desist from malicious activities like denial of service, brute force and many attacks. Keywords— Botnet, P2P, Network traffic, Cryptographic protocol, Content auditing.