Article: Deep learning-based approach for malware classification Journal: International Journal of Intelligent Defence Support Systems (IJIDSS) 2021 Vol.6 No.2 pp.61 - 80 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. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-based approach for malware classification

Authors: Harisha Airbail; G. Mamatha; Rahul V. Hedge; P.R. Sushmika; Reshma Kumari; K. Sandeep

Addresses: Department of CS&E, Sahyadri College of Engineering and Management, Managaluru, India ' Department of ISE, RNS Institute of Technology, Bengaluru, India ' Department of CS&E, Sahyadri College of Engineering and Management, Managaluru, India ' Department of CS&E, Sahyadri College of Engineering and Management, Managaluru, India ' Department of CS&E, Sahyadri College of Engineering and Management, Managaluru, India ' Department of CS&E, Sahyadri College of Engineering and Management, Managaluru, India

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.

Keywords: malware; deep learning; threat identification; intelligent systems.

DOI: 10.1504/IJIDSS.2021.115226

International Journal of Intelligent Defence Support Systems, 2021 Vol.6 No.2, pp.61 - 80

Received: 12 Jul 2020
Accepted: 16 Nov 2020
Published online: 24 May 2021 *

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Title: Deep learning-based approach for malware classification

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