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Pattern recognition techniques have been widely used in security-sensitive applications to distinguish malicious samples from legitimate ones. However, there usually exist some intelligent attackers who intend to have malicious samples to be mis-classified as legitimate at test time, i.e. evasion attack. Current researches show that traditional Support Vector Machines (SVMs) are vulnerable to evasion...
Many studies have shown that Multiple Classifier Systems (MCSs) are more robust than single classifiers to evasion attacks for linear classifiers. However, to the best of our knowledge, the robustness of MCSs for non-linear classifiers has not been inves-tigated. This paper attempts to discuss two issues experimentally including a MCS is still more robust than a single classifier for non-linear classifiers,...
Malicious websites provide a platform supporting diverse Internet crimes. They do not only steal the sensitive information but also let the hacker to control the computer of users. Malicious website detection with the machine learning technique achieves satisfying result. However, the characteristics of the malicious website may be modified to evade the detection. In this paper, the exploratory attack...
Biomarker identification and cancer classification are two closely related problems. In gene expression data sets, the correlation between genes can be high when they share the same biological pathway. Moreover, the gene expression data sets may contain outliers due to either chemical or electrical reasons. A good gene selection method should take group effects into account and be robust to outliers...
MicroRNA (miRNA), which is short non-coding RNA, plays important roles in almost all biological processes examined. Several classifiers have been applied to predict humans, mice and rats precursor miRNAs (pre-miRNAs), but no classifier is applied to classify porcine pre-miRNAs only based on the porcine pre-miRNAs because of little known miRNA component in the porcine genome. Here, we developed a novel...
Support vector machine (SVM) is an algorithm based on structure risk minimizing principle and has high generalization ability, but sometimes we prefer to incremental learning algorithms to handle very vast data for training SVM is very costly in time and memory consumption or because the data available are obtained at different intervals. SVM works well for incremental learning model with impressive...
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