NDLI: Dynamic Innate Immune System Model for Malware Detection

Content Provider	IEEE Xplore Digital Library
Author	Ali, M.A.M. Maarof, M.A.
Copyright Year	2013
Description	Author affiliation: Fac. of Math. Sci., Univ. of Khartoum, Khartoum, Sudan (Ali, M.A.M.) \|\| Fac. of Comput., Univ. Teknol. Malaysia, Skudai, Malaysia (Maarof, M.A.)
Abstract	Malware stand for Malicious Software became a major threat facing the massive amount of data transmitted through the internet and the systems holding that data. Malware detection is the process of identifying the malicious behavior or object as malware. Many methods used to do the detection process, these methods are varied depending on the process used by the detector -anti virus or anti malware is a commercial name of detectors. Signature base, behavior base and specification base. Increasing the detection accuracy is the main goal of researchers in the last decade. In this paper we introduce a dynamic malware detection model by applying the innate immune system to improve the detection accuracy. The proposed model applied to the portable executable file representation by extracting the API call logs from new installed windows environment due to the wide spread of this type of files in different platforms. The results of the experiments show a better detection accuracy of the proposed model for known malware and promising improvement on the new unknown malware and polymorphic malware.
Starting Page	1
Ending Page	4
File Size	201082
Page Count	4
File Format	PDF
ISBN	9781479928453
DOI	10.1109/ICITCS.2013.6717828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Accuracy Computational modeling Trojan horses Grippers Immune system
Content Type	Text
Resource Type	Article

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