Deep learning-based approach for malware classification. (21st May 2021)

• Record Type: Journal Article
• Authors: Airbail, Harisha; Mamatha, G.; Hedge, Rahul V.; Sushmika, P.R.; Kumari, Reshma; Sandeep, K.
• Is Part Of: International journal of intelligent defence support systems. Volume 6:Number 2(2021)
• Page Start: 61
• Page End: 80
• Publication Date: 2021-05-21
• Subjects: malware -- deep learning -- threat identification -- intelligent systems; Artificial intelligence -- Military applications -- Periodicals; Cybernetics -- Periodicals; Experimental design -- Periodicals; 623.028563
• Languages: English
• ISSNs: 1755-1587

Abstract

Any program that exhibit furtive demonstrations against the interests of the PC client can be considered as a malware. These baleful programs can play out varieties of different capacities, for example, taking, encoding, or erasing dainty information, changing or commandeering centre processing capacities, and examining clients' computer action without their consent. Today, malware is utilised by both governments and black hat hackers, to take individual, financial, or business data. In this paper, put forward a strategy for arranging malware utilising profound learning procedures. Malware binaries are pictured as greyscale pictures, with the perception that for some malware families, the pictures having a place with a similar family show up fundamentally the same as in surface and design. A standard picture highlights grouping strategy is proposed. The exploratory outcomes give 97.45% arrangement classification on a malware database of 9, 339 examples with 25 diverse malware families.