Botnet C&C Traffic and Flow Lifespans Using Survival Analysis

Abstract

This paper addresses the issue of detecting unwanted traffic in data networks, namely the detection of botnet networks. In this paper, we focused on a time behavioral analysis, more specifically said – lifespans of a simulated botnet network traffic, collected and discovered from NetFlow messages, and also of real botnet communication of a malware. As a method we chose survival analysis and for rigorous testing of differences Mantel–Cox test. Lifespans of those referred traffics are discovered and calculated by lifelines using Python language. Based on our research we have figured out a possibility to distinguish the individual lifespans of C&C communications that are identical to each other by using survival projection curves, although it occurred in a different time course.

This paper addresses the issue of detecting unwanted traffic in data networks, namely the detection of botnet networks. In this paper, we focused on a time behavioral analysis, more specifically said – lifespans of a simulated botnet network traffic, collected and discovered from NetFlow messages, and also of real botnet communication of a malware. As a method we chose survival analysis and for rigorous testing of differences Mantel–Cox test. Lifespans of those referred traffics are discovered and calculated by lifelines using Python language. Based on our research we have figured out a possibility to distinguish the individual lifespans of C&C communications that are identical to each other by using survival projection curves, although it occurred in a different time course.