B-CAT: a model for detecting botnet attacks using deep attack behavior analysis on network traffic flows

Abstract

Threats on computer networks have been increasing rapidly, and irresponsible parties are always trying to exploit vulnerabilities in the network to do various dangerous things. One way to exploit vulnerabilities in a computer network is by employing malware. Botnets are a type of malware that infects and attacks targets in groups. Botnets develop quickly; the characteristics of initially sporadic attacks have grown into periodic and simultaneous. This rapid development has proved that the botnet is advanced and requires more attention and proper handling. Many studies have introduced detection models for botnet attack activity on computer networks. Apart from detecting the presence of botnet attacks, those studies have attempted to explore the characteristics of botnets, such as attack intensity, relationships between activities, and time segment analysis. However, there has been no research that explicitly detects those characteristics. On the other hand, each botnet characteristic requires different handling, while recognizing the characteristics of the botnet can help network administrators make appropriate decisions. Based on these reasons, this research builds a detection model that can recognize botnet characteristics using sequential traffic mining and similarity analysis. The proposed method consists of two main processes. The first is training to build a knowledge base, and the second is testing to detect botnet activity and attack characteristics. It involves dynamic thresholds to improve the model sensitivity in recognizing attack characteristics through similarity analysis. The novelty includes developing and combining analytical techniques of sequential traffic mining, similarity analysis, and dynamic threshold to detect and recognize the characteristics of botnet attacks explicitly on actual behavior in network traffic. Extensive experiments have been conducted for the evaluation using three different datasets whose results show better performance than others.