Journals and Books

Detecting Botnet Traffic from a Single Host

Detecting Botnet Traffic from a Single Host

The detection of bots and botnets in the network may be improved if the analysis is done on the traffic of one bot alone. While a botnet may be detected by correlating the behavior of several bots in a large amount of traffic, one bot alone can be detected by analyzing its unique trends in less traffic. The algorithms to differentiate the traffic of one bot from the normal traffic of one computer may take advantage of these differences. The authors propose to detect bots in the network by analyzing the relationships between flow features in a time window. The technique is based on the Expectation-Maximization clustering algorithm. To verify the method they designed test-beds and obtained a dataset of six different captures. The results are encouraging, showing a true positive error rate of 99.08% with a false positive error rate of 0.7%.

Observer effect: How Intercepting HTTPS traffic forces malware to change their behavior

During the last couple of years there has been an important surge on the use of HTTPs by malware. The reason for this increase is not completely understood yet, but it is hypothesized that it was forced by organizations only allowing web traffic to the Internet. Using HTTPs makes malware behavior similar to normal connections. Therefore, there has been a growing interest in understanding the usage of HTTPs by malware. This paper describes our research to obtain large quantities of real malware traffic using HTTPs, our use of man-in-the-middle HTTPs interceptor proxies to open and study the content, and our analysis of how the behavior of the malware changes after being intercepted. The research goal is to understand how malware uses HTTPs and the impact of intercepting its traffic. We conclude that the use of an interceptor proxy forces the malware to change its behavior and therefore should be carefully considered before being implemented.

An Empirical Comparison of Botnet Detection Methods

The results of botnet detection methods are usually presented without any comparison. Although it is generally accepted that more comparisons with third-party methods may help to improve the area, few papers could do it. Among the factors that prevent a comparison are the difficulties to share a dataset, the lack of a good dataset, the absence of a proper description of the methods and the lack of a comparison methodology. This paper compares the output of three different botnet detection methods by executing them over a new, real, labeled and large botnet dataset.

Survey on Network-based Botnet Detection Methods.

Botnets are an important security problem on the Internet. They continuously evolve their structure, protocols and attacks. This survey analyzes and compares the most important efforts done in the network-based detection area. It accomplishes four tasks: first, the comparison of previous surveys and the proposal of four new dimensions to analyze their classification schemes. Second, a new classification and comparison of network-based botnet detection proposals, that includes the definition of twenty desired properties of every botnet detection paper. Third, an extensive comparison between the most representative detection proposals. Fourth, the description of the most important problems and highlights in the area. We conclude that the area has achieved great advances so far, but there are still many open problems.

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