Pengtao Zhang

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A malware detection model based on a negative selection algorithm with penalty factor (NSAPF) is proposed in this paper. This model extracts a malware instruction library (MIL), containing instructions that tend to appear in malware, through deep instruction analysis with respect to instruction frequency and file frequency. From the MIL, the proposed model(More)
Enantioselective total syntheses of katsumadain and katsumadain C were achieved concisely through a biomimetic approach. Assembly of styryl-2-pyranone (3) and monoterpene 6 via acid-promoted regio- and stereoselective C-C bond formation afforded katsumadain (2), which underwent the photoinduced [2 + 2] dimerization in a head-to-tail mode to furnish(More)
As viruses become more complex, existing antivirus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. Inspired by immune system, a hierarchical artificial immune system (AIS) model, which is based on matching in three layers, is proposed to detect a variety of forms of viruses. In the bottom layer, a(More)
This paper proposes a multi-resolution-concentration (MRC) based feature construction approach for spam filtering by progressively partitioning an email into local areas on smaller and smaller resolutions. The MRC approach depicts a dynamic process of gradual refinement in locating the pathogens by calculating concentrations of detectors on local areas, and(More)
This paper proposes a novel feature construction approach based on term space partition (TSP) aiming to establish a mechanism to make terms play more sufficient and rational roles in email categorization. Dominant terms and general terms are separated by performing a vertical partition of the original term space with respect to feature selection metrics,(More)
In this paper, a hybrid concentration based feature extraction (HCFE) approach is proposed. The HCFE approach extracts the hybrid concentration (HC) of a sample in both the global resolution and the local resolution. The HC of a sample characterizes the sample more precisely and completely by taking the global information and local information into account(More)
This paper proposes a new feature-goodness criterion named class-wise information gain (CIG). The CIG is able to measure the goodness of a feature for recognizing a specific class, and further helps to select the features with the highest information content for a specific class. In order to confirm the effectiveness of the CIG, a CIG-based malware(More)