Winning with DNS Failures: Strategies for Faster Botnet Detection

Abstract. Botnets such as Conficker and Torpig utilize high entropy domains for fluxing and evasion. Bots may query a large number of domains, some of which may fail. In this paper, we present techniques where the failed domain queries (NXDOMAIN) may be utilized for: (i) Speeding up the present detection strategies which rely only on successful DNS domains. (ii) Detecting Command and Control (C&C) server addresses through features such as temporal correlation and information entropy of both successful and failed domains. We apply our technique to a Tier-1 ISP dataset obtained from South Asia, and a campus DNS trace, and thus validate our methods by detecting Conficker botnet IPs and other anomalies with a false positive rate as low as 0.02%. Our technique can be applied at the edge of an autonomous system for real-time detection

Botnet detection techniques: review, future trends, and issues

NoIn recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.University of Malaya, Malaysia (No. FP034-2012A