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The proposed non-signature based system creates a meta feature space for the detection of metamorphic malware samples where three sets of features are extracted from the files: (a) branch opcodes (b) unigrams (c) bigrams. The feature space is initially pruned using Naïve Bayes method. After the rare feature elimination process, the relevant opcodes that are highly contributing towards the target class...
In this paper we analyze the advantages of using data mining techniques and tools for data fusion in forensic speaker recognition. Segmental and suprasegmental features were employed in 28 different classifiers, in order to compare their performances. The selected classifiers have different learning techniques: lazy or instance-based, eager and ensemble. Two approaches were employed on the classification...
In this article, a non-signature based statistical scanner for metamorphic malware detection, employing feature ranking methods like Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF), Galavotti-Sebastiani-Simi Coefficient (GSS), Term Significance (TS) and Odds Ratio (OR) is proposed. Malware and benign models for classification are created by considering top ranked features obtained...
To unfold a solution for the detection of metamorphic viruses (obfuscated malware), we propose a non signature based approach using feature selection techniques such as Categorical Proportional Difference (CPD), Weight of Evidence of Text (WET), Term Frequency-Inverse Document Frequency (TF-IDF) and Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF). Feature selection methods are...
A graph based classifier is proposed to recognize the different time phases of the up & go test based on signals collected by an inertial sensor set on a person chest. This test being a sequential set of actions, a graph is used to model it and enforce the classification algorithm to estimate a solution with this constraint. The graph is described by a Markov chain A(m). Based on the hidden Markov...
Brain Computer Interfaces has some exciting prospects such as controlling devices at the speed of thought. However BCI technology is far from attaining this goal. A significant challenge the EEG-based system has is the interference of artifacts in the EEG generated by eye and head movement. This paper presents the use of machine learning techniques to classify artifacts in the EEG. Successful artifact...
Using Boolean AND and OR functions to combine the responses of multiple one- or two-class classifiers in the ROC space may significantly improve performance of a detection system over a single best classifier. However, techniques found in literature assume that the classifiers are conditionally independent, and that their ROC curves are convex. These assumptions are not valid in most real-world applications,...
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