Unwanted Traffic Identification in Large-Scale University Networks: A Case Study

Abstract

To mitigate the malicious impact of P2P traffic on University networks, in this article the authors have proposed the design of payload-oblivious privacy-preserving P2P traffic detectors. The proposed detectors do not rely on payload signatures, and hence, are resilient to P2P client and protocol changes—a phenomenon which is now becoming increasingly frequent with newer, more popular P2P clients/protocols. The article also discusses newer designs to accurately distinguish P2P botnets from benign P2P applications. The datasets gathered from the testbed and other sources range from Gigabytes to Terabytes containing both unstructured and structured data assimilated through running of various applications within the University network. The approaches proposed in this article describe novel ways to handle large amounts of data that is collected at unprecedented scale in authors’ University network.