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.