network flow

Botnets are known to be among the most popular malwares in cyber criminals for their practicality in carrying many cyber-crimes as reported in the recent news. While many detection schemes have been developed, botnets remain the most powerful attack platform by constantly and continuously adopting new techniques and strategies. Thus, early identification and timely detection of botnets can take an effective step towards making perfect defense system. Most of existing botnet detection methods cannot detect botnets in real-time and in an early stage of their lifecycle before participating in a cyber-crime. In this work, we propose a novel approach to detect the BlackEnergy botnet traffic using Hidden Markov Model (HMM) within flow Intervals. In BlackEnergy, bots are controlled by attackers under a HTTP base command and control (C&C) infrastructure. First we analysis BlackEnergy’s network traffic and extract its main features and network behavior patterns. Then we adapt the proposed HMM model with BlackEnergy botnet patterns and features.  In addition to detecting the botnet communication traffic in both Attack and C&C stages, inferred HMM defines the stage of botnet lifecycle. Our proposed method detects botnet activity in small time intervals without having seen a complete network flow. Using existing datasets, we show experimentally that it is possible to identify the presence of botnets activity with high accuracy even in very small time windows.