Multilayer Framework for Botnet Detection Using Machine Learning Algorithms

Abstract

A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. The traditional method of detecting botnets commonly used signature-based analysis unable to detect unseen botnets. The behavior-based analysis seems like a promising solution to the current trends of botnets that keep evolving. This paper proposes a multilayer framework for botnet detection using machine learning algorithms that consist of a filtering module and classification module to detect the botnet’s command and control server. We highlighted several criteria for our framework, such as it must be structure-independent, protocol-independent, and able to detect botnet in encapsulated technique. We used behavior-based analysis through flow-based features that analyzed the packet header by aggregating it to a 1-s time. This type of analysis enables detection if the packet is encapsulated, such as using a VPN tunnel. We also extend the experiment using different time intervals, but a 1-s time interval shows the most impressive results. The result shows that our botnet detection method can detect up to 92% of the f-score, and the lowest false-negative rate was 1.5%.

Keywords

• Behavior-based analysis
• botnet
• flow-based feature selection
• k-nearest neighbor
• structure independent