A Multi-Perspective malware detection approach through behavioral fusion of API call sequence

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248758" target="_blank" >RIV/61989100:27240/21:10248758 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/21:10248758

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S016740482100273X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S016740482100273X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cose.2021.102449" target="_blank" >10.1016/j.cose.2021.102449</a>

Result language

angličtina

Original language name

A Multi-Perspective malware detection approach through behavioral fusion of API call sequence

Original language description

The widespread development of the malware industry is considered the main threat to our e-society. Therefore, malware analysis should also be enriched with smart heuristic tools that recognize malicious behaviors effectively. Although the generated API calling graph rep-resentation for malicious processes encodes worthwhile information about their malicious behavior, it is pragmatically inconvenient to generate a behavior graph for each process. Therefore, we experimented with creating generic behavioral graph models that describe malicious and non-malicious processes. These behavioral models relied on the fusion of statistical, contextual, and graph mining features that capture explicit and implicit rela-tionships between API functions in the calling sequence. Our generated behavioral models proved the behavioral contrast between malicious and non-malicious calling sequences. According to that distinction, we built different relational perspective models that charac-terize processes&apos; behaviors. To prove our approach novelty, we experimented with our ap-proach over Windows and Android platforms. Our experimentations demonstrated that our proposed system identified unseen malicious samples with high accuracy with low false -positive. In terms of detection accuracy, our model retums an average accuracy of 0.997 and 0.977 to the unseen Windows and Android malware testing samples, respectively. More -over, we proposed a new indexing method for APIs based on their contextual similarities. We also suggested a new expressive, a visualized form that renders the API calling sequence. Consequently, we introduced a confidence metric to our model classification decision. Fur-thermore, we developed a behavioral heuristic that effectively identified malicious API call sequences that were deceptive or mimicry. (c) 2021 Elsevier Ltd. All rights reserved.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers and Security

ISSN

0167-4048

e-ISSN

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Volume of the periodical

110

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

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UT code for WoS article

000703432300007

EID of the result in the Scopus database

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