Name
Abstract | ||
|---|---|---|
Anti-malware tools remain the primary line of defense against malicious software. There is a wide variety of commercial anti-malware tools in the IT security market. However, no single tool is able to provide a full protection against the overwhelming number of daily released malware. Hence, collaboration among malware detection tools is of paramount importance. In this paper, we propose MACoMal, a multi-agent based decision mechanism, which assists heterogeneous anti-malware tools to collaborate with each other in order to reach a consensual decision about the maliciousness of a suspicious file. MACoMal consists of two main elements: (1) an executable file identification model, and (2) a collaborative decision-making scheme. MACoMal is analyzed with respect to network connectivity and global decision correctness. By leveraging a multi-agent simulation tool and a set of real malware samples, we present a simulation methodology to assess its effectiveness and efficiency. Experimental results show that MACoMal is able to immunize a network against a malware threat within a time that ranges from a few seconds to a few minutes after the threat detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2966321 | IEEE ACCESS |
Keywords | DocType | Volume |
Malware,anti-malware assistance,multi-agent systems,modelling,analysis,simulation,collaboration | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Belaoued | 1 | 1 | 0.69 |
| Abdelouahid Derhab | 2 | 277 | 32.68 |
| Smaine Mazouzi | 3 | 23 | 9.40 |
| Farrukh Aslam Khan | 4 | 388 | 34.17 |