  Over the past decade, the Cyberspace has seen an increasing number of attacks
coming from botnets using the Peer-to-Peer (P2P) architecture. Peer-to-Peer
botnets use a decentralized Command & Control architecture. Moreover, a large
number of such botnets already exist, and newer versions- which significantly
differ from their parent bot- are also discovered practically every year. In
this work, the authors propose and implement a novel hybrid framework for
detecting P2P botnets in live network traffic by integrating Neural Networks
with Bayesian Regularization. Bayesian Regularization helps in achieving better
generalization of the dataset, thereby enabling the detection of botnet
activity even of those bots which were never used in training the Neural
Network. Hence such a framework is suitable for detection of newer and unseen
botnets in live traffic of a network. This was verified by testing the
Framework on test data unseen to the Detection module (using untrained botnet
dataset), and the authors were successful in detecting this activity with an
accuracy of 99.2 %.
