Visible to the public Malware Detection Approach Based on the Swarm-Based Behavioural Analysis over API Calling Sequence

TitleMalware Detection Approach Based on the Swarm-Based Behavioural Analysis over API Calling Sequence
Publication TypeConference Paper
Year of Publication2022
AuthorsAmer, Eslam, Samir, Adham, Mostafa, Hazem, Mohamed, Amer, Amin, Mohamed
Conference Name2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)
Date Publishedmay
KeywordsAnt Colony, API calling sequence, Behavioral sciences, computer viruses, Databases, dynamic analysis, graph theory, Human Behavior, machine learning, Malware, malware analysis, Metrics, Organizations, privacy, pubcrawl, resilience, Resiliency, Resiliency Coordinator, ubiquitous computing, word embedding
AbstractThe rapidly increasing malware threats must be coped with new effective malware detection methodologies. Current malware threats are not limited to daily personal transactions but dowelled deeply within large enterprises and organizations. This paper introduces a new methodology for detecting and discriminating malicious versus normal applications. In this paper, we employed Ant-colony optimization to generate two behavioural graphs that characterize the difference in the execution behavior between malware and normal applications. Our proposed approach relied on the API call sequence generated when an application is executed. We used the API calls as one of the most widely used malware dynamic analysis features. Our proposed method showed distinctive behavioral differences between malicious and non-malicious applications. Our experimental results showed a comparative performance compared to other machine learning methods. Therefore, we can employ our method as an efficient technique in capturing malicious applications.
Citation Keyamer_malware_2022