The threats of botnets are becoming a growing concern infecting more and more computers every day. Although botnets can be detected from their behavioral patterns, it is becoming more challenging to differenti-ate the behavior between the malicious traffic and the legitimate trafficas with the advancement of the technologies the malicious traffics are fol-lowing the similar behavioral patterns of benign traffics. The detectionof malicious traffic largely depends on the traffic features that are beingused to feed in the detection process. Selecting the best features for effec-tive botnet detection is the main contribution of this paper. At the verybeginning, we show the impact of different features on botnet detectionprocess. Then we propose several heuristics to select the best featuresfrom a handful of possible features. Some proposed heuristics are trulyfeature-based and some are group-based, thus generating different accu-racy levels. We also analyze time complexity of each heuristic and providea detailed performance analysis. As working with all combinations of alarge number of features is not feasible, some heuristics work by groupingthe features based on their similarity in patterns and checking all combi-nations within the groups of small number of features which improves thetime complexity by a large margin. Through experiments we show the efficacy of the proposed feature selection heuristics. The result shows thatsome heuristics outperform state-of-the-art feature selection algorithms.

On Feature Selection Algorithms for Effective Botnet Detection the features based on their similarity in patterns and checking all combinations within the groups of small number of features which improves the time complexity by a large margin. Through experiments we show the efficacy of the proposed feature selection heuristics. The result shows that some heuristics outperform state-of-the-art feature selection algorithms.