Contextual identification of windows malware through semantic interpretation of API call sequence

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246992" target="_blank" >RIV/61989100:27240/20:10246992 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/10/21/7673" target="_blank" >https://www.mdpi.com/2076-3417/10/21/7673</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app10217673" target="_blank" >10.3390/app10217673</a>

Result language

angličtina

Original language name

Contextual identification of windows malware through semantic interpretation of API call sequence

Original language description

The proper interpretation of the malware API call sequence plays a crucial role in identifying its malicious intent. Moreover, there is a necessity to characterize smart malware mimicry activities that resemble goodware programs. Those types of malware imply further challenges in recognizing their malicious activities. In this paper, we propose a standard and straightforward contextual behavioral models that characterize Windows malware and goodware. We relied on the word embedding to realize the contextual association that may occur between API functions in malware sequences. Our empirical results proved that there is a considerable distinction between malware and goodware call sequences. Based on that distinction, we propose a new method to detect malware that relies on the Markov chain. We also propose a heuristic method that identifies malware&apos;s mimicry activities by tracking the likelihood behavior of a given API call sequence. Experimental results showed that our proposed model outperforms other peer models that rely on API call sequences. Our model returns an average malware detection accuracy of 0.990, with a false positive rate of 0.010. Regarding malware mimicry, our model shows an average noteworthy accuracy of 0.993 in detecting false positives. (C) 2020 by the authors. Licensee MDPI, Basel, Switzerland.

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

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

15

Pages from-to

1-15

UT code for WoS article

000589006900001

EID of the result in the Scopus database

2-s2.0-85096004091