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- W2018257180 abstract "In this paper, we show that a Bio-inspired classifier's accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent features for the well-known problem of malware portscan detection. We compare the performance of three well-known Bio-inspired classifiers operating on the proposed intelligent features: (1) Real Valued Negative Selection (RVNS) based on the adaptive immune system; (2) Dendritic Cell Algorithm (DCA) based on the innate immune system; and (3) Adaptive Neuro Fuzzy Inference System (ANFIS). To empirically evaluate the improvements provided by the intelligent features, we use a network traffic dataset collected on diverse endpoints for a period of 12 months. The endpoints' traffic is infected with well-known malware. For unbiased performance comparison, we also include a machine learning algorithm, Support Vector Machine (SVM), and two state-of-the-art statistical malware detectors, Rate-Limiting (RL) and Maximum-Entropy (ME). To the best of our knowledge, this is the first study in which RVNS and DCA are not only compared with each other but also with several other classifiers on a comprehensive real-world dataset. The experimental results indicate that our proposed features significantly improve the TP rate and FP rate of both RVNS and DCA." @default.
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- W2018257180 modified "2023-09-23" @default.
- W2018257180 title "Improving accuracy of immune-inspired malware detectors by using intelligent features" @default.
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- W2018257180 doi "https://doi.org/10.1145/1389095.1389112" @default.
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