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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/21:10248758

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Computers and Security

ISSN

0167-4048

e-ISSN

—

Svazek periodika

110

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

—

Kód UT WoS článku

000703432300007

EID výsledku v databázi Scopus

—