Modern botnet trends have lead to the use of IP and domain fast-fluxing to avoid detection and increase resilience. These techniques bypass traditional detection systems such as blacklists and intrusion detection systems. The Domain Name Service (DNS) is one of the most prevalent protocols on modern networks and is essential for the correct operation of many network activities, including botnet activity. For this reason DNS forms the ideal candidate for monitoring, detecting and mitigating botnet activity. In this paper a system placed at the network edge is developed with the capability to detect fast-flux domains using DNS queries. Multiple domain features were examined to determine which would be most effective in the classification of domains. This is achieved using a C5.0 decision tree classifier and Bayesian statistics, with positive samples being labeled as potentially malicious and negative samples as legitimate domains. The system detects malicious domain names with a high degree of accuracy, minimising the need for blacklists. Statistical methods, namely Naive Bayesian, Bayesian, Total Variation distance and Probability distribution are applied to detect malicious domain names. The detection techniques are tested against sample traffic and it is shown that malicious traffic can be detected with low false positive rates.
Similarly, Stalmans et al. REF used the C5.0 decision tree method and Bayesian statistics to classify automatically generated domain names with legitimate domain names.
A framework for DNS based detection and mitigation of malware infections on a network
2011 Information Security for South Africa
2011 Information Security for South Africa
Computer Science