Journal
CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY
Volume -, Issue -, Pages 1748-1750Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2976749.2989055
Keywords
Malware Detection; Ensemble Pruning; Pareto Bi-objective Optimization; Begonia
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This paper proposes Begonia, a malware detection system through Pareto ensemble pruning. We convert the malware detection problem into the bi-objective Pareto optimization, aiming to trade off the classification accuracy and the size of classifiers as two objectives. We automatically generate several groups of base classifiers using SVM and generate solutions through bi-objective Pareto optimization. We then select the ensembles with highest accuracy of each group to form the final solutions, among which we hit the optimal solution where the combined loss function is minimal considering the trade-off between accuracy and time cost. We expect users to provide different trade-off levels to their different requirements to select the best solution. Experimental results show that Begonia can achieve higher accuracy with relatively lower overhead compared to the ensemble containing all the classifiers and can make a good trade-off to different requirements.
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