Conference proceeding
A survey on graph-based methods for malware detection

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Publication Details
Editor list: Aya Hellal, Fatma Mallouli, Adel Hidri, Rami Khalaf Aljamaeen
Publisher: IEEE
Place: Hammamet, Tunisia
Publication year: 2020
Title of series: IC_ASET
Number of pages: 5
Web of Science ID:
PubMed ID:
Scopus ID: 85100622118

The widespread presence of malware is causing economic loss to organizations and individuals. For this reason, detecting malware has gained great interest as part of a computer security topic. Most current malware detection software uses signature-based methods to identify threats. However, this syntactic approach fails to identify variants of known malware or previously undiscovered malware. Since graphs represent a strong tool for modeling the sophisticated behaviors of malware and it is harder for an attacker to radically change the behavior of malware than to morph its syntactic structure, many graph-based methods are proposed to overcome the disadvantages of the traditional approach. In this paper, we present a survey of graph-based approaches for malware detection.

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Last updated on 2021-12-10 at 09:24