Structural Feature Engineering approach for detecting polymorphic malware
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Currently, malware are distributed in a
polymorphic form. There are very smart and obfuscated.
This serves the purpose of hardening detection or simply
making it impossible. Researchers have mainly resorted to
static analysis, dynamic analysis or a combination of both
in attempting to find advanced solutions to polymorphic
malware detection problems. This paper presents a novel
simple feature engineering approach in terms of extracting,
analyzing and processing static based features for efficient
detection of polymorphic malware. K-NN algorithm is used
to build the detection model. Our experiments achieve a
detection accuracy of 98.7% with 0.014% False Positive
Rate (FPR) on a relatively small dataset.
Description
Keywords
Polymorphic malware, Static analysis, Machine learning
Citation
Masabo, E., Kaawaase, K. S., Sansa-Otim, J., & Hanyurwimfura, D. (2017, November). Structural Feature Engineering approach for detecting polymorphic malware. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 716-721). IEEE.