Malicious program ontology rule set based on association decision and linear discriminant Online publication date: Fri, 01-Mar-2024
by Chenghua Tang; Min Hu; Mengmeng Yang; Baohua Qiang
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 16, No. 2, 2024
Abstract: Aiming at the problems of poor scalability and long-time consumption in building inference rule sets manually for malware domain ontology, an automatic generation method for malware ontology rule sets is proposed. We extract the behaviour characteristics of malicious programs by defining a formal extended description method based on the frequency of API calls of malicious programs and combining the frequency of API functions. Based on association rules and decision trees, the behaviour characteristics of malicious programs are mined to form a fine-grained redefined rule set of malicious program categories, and SWRL rule language is used to semantic transform the rule set. In addition, the coarse granularity classification of program behaviour rules is implemented based on Fisher linear discriminant algorithm. The generation efficiency of malware ontology rules generated by us is 10.08 pieces/second, and the inference detection rate of unknown samples reaches 89.92%.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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:
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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook