Improving accuracy of immune-inspired malware detectors by using intelligent features

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 stateof-the-art statistical malware detectors, Rate-Limiting (RL) and Maximum-E...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: M. Zubair Shafiq, Syed Ali Khayam, Muddassar Farooq
• Keywords: Dendritic Cell Algorithm, GECCO 2008, Immune System, Intelligent Features, Optimization