Analysis of Attacker Behavior in Compromised Hosts During Command and Control
This work provides a proactive method for cybersecurity professionals to detect botnets, though it is incremental by focusing on shell commands instead of network data.
The paper tackled the problem of detecting botnet hosts by analyzing attacker command sequences in compromised Linux shells, achieving successful prediction of attacker behavior without relying on network traffic analysis.
Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral analysis of botnet is shown to be effective against a wide variety of attack types. Current works, however, focus solely on analyzing network traffic from and to the bots. In this work we take a different approach of analyzing the chain of commands input by attackers in a compromised host. We have deployed several honeypots to simulate Linux shells and allowed attackers access to the shells to collect a large dataset of commands. We have further developed an automated mechanism to analyze these data. For the automation we have developed a system called CYbersecurity information Exchange with Privacy (CYBEX-P). Finally, we have done a sequential analysis on the dataset to show that we can successfully predict attacker behavior from the shell commands without analyzing network traffic like previous works.