@inproceedings{0ceb2a19a3af4a18829d9cd706672e8d,
title = "A survey on graph-based methods for malware detection",
abstract = "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.",
keywords = "Dynamic analysis, Graphs, Malware detection, Static analysis",
author = "Aya Hellal and Fatma Mallouli and Adel Hidri and Aljamaeen, \{Rami Khalaf\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th International Conference on Advanced Systems and Emergent Technologies, IC\_ASET 2020 ; Conference date: 15-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
day = "15",
doi = "10.1109/IC\_ASET49463.2020.9318301",
language = "English",
series = "Proceedings of the International Conference on Advanced Systems and Emergent Technologies, IC\_ASET 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "130--134",
editor = "Amor, \{Abdessattar Ben\} and Salwa Elloumi and Samir Nejim and Nabila Dhahbi and Hassene Gritli and Naoufel Machta",
booktitle = "Proceedings of the International Conference on Advanced Systems and Emergent Technologies, IC\_ASET 2020",
}