Malware detection model based on classifying system calls and code attributes: a proof of concept
by Malik F. Saleh
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 11, No. 2, 2019

Abstract: The process of malware detection involves static code analysis and dynamic analysis. Both methods have limitations. This research tried to bridge the gap between the two methods by dynamically predicting the risk before the static analysis. The proof-of-concept examined the code of known malwares and concluded that five characteristics of the code will predict the risk of any executable file, namely, the system function, encryption, code obfuscation, stalling code, and checking for the debugger environment. The proof-of-concept validates the effectiveness of the model. It shows 96% success and limited false-positives results.

Online publication date: Tue, 02-Apr-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Electronic Security and Digital Forensics (IJESDF):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email