IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 11 NO. 1 2025
DOI: 10.56201/ijcsmt.v11.no1.2025.pg97.114

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Leveraging Application Programming Interface (API) Call Patterns for Real-Time Dynamic Malware Detection Using Deep Learning

Uzodinma, Victor Chukwudi

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Abstract

With the rise in new malware threats in recent years, where data security and response time are crucial for both businesses and home users, the situation is expected to worsen. Despite the widespread use of anti-malware software, malware infections continue to grow rapidly. One concerning method is dynamic malware attacks through API calls, where malicious code interacts with an application's APIs in real-time. The attacker exploits vulnerabilities in the application or its infrastructure to access sensitive data or take control of the system. These attacks are often aimed at stealing credentials, executing unauthorized commands, or installing additional malware. To address the issue of dynamic malware attacks through API calls, this dissertation introduces a technique for detecting and classifying such attacks.

keywords:

API Call Pattern, Real-Time, Malware

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References:

Burnap, P., French, R., Turner, F. & Jones, K. (2018). Malware classification using self 859
organizing feature maps and machine activity data. Computer Security, 73, 399–410.
Elhadi, A. A. E., Maarof, M. A. & Barry, B. I. (2013). Improving the detection of malware be-
874 haviour using simplified data dependent API call graph. International Journal
Security Application, 7 (5), 875 29–42.
Eslam, A. & Ivan, Z. (2018). A dynamic Windows malware detection and prediction method
based on contextual understanding of API call sequence. Computers & Security, 30(40),
1-15.
Gandotra, E., Bansal, D. & Sofat, S. (2014). Malware analysis and classification: a survey. 885
Journal of Information Security, 5 (02), 56.
Gibert, D., Mateu, C. & Planes, J. (2020). The rise of machine learning for detection and
classification of malware: Research developments, trends and challenges. Journal of
Network and Computer Applications, 153(2020), 1-22, 2020.
Karbab, E. B., Debbabi, M., Derhab, A. & Mouheb, D. (2018). MalDozer: Automatic
framework for android malware detection using deep learning, Digital Investigation 24,
548-559.
Kim, T., Kang, B., Rho, M., Sezer, S. & Gyu, E. (2019). A Multimodal Deep Learning Method
for Android Malware Detection using Various Features, in IEEE Transactions on
Information Forensic and Security, 10(3), 773-778.
Li, J., Sunk, L., Yan, Q., Zhiqiang, L. Srisaan, W. & Heng, Y. (2018). “Significant Permission
Identification for Machine Learning Based Android Malware Detection”, in IEEE
Transactions on Industrial Informatics, 14(7), 3216-3225.
Mario, L., Marta, C., Damiano, D., Fabio, M. & Francesco, M. (2019). Dynamic malware
detection and phylogeny analysis using process mining. International Journal of
Information Security, 18, 257–284.
McLaughlin, N. Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., Safaei, Y., Trickel, E.,
Zhao, Z., Doupe, A, & Ahn, G. (2017). Deep Android Malware Detection, Proceeding
on the Seventh ACM on Conference on Data and Application Security and Privacy,
301-308.
Nighat, U., Saeeda, U., Fazlullah, K., Mian, A., Ahthasham S., Mamoun A., Paul W. (2021).
Intelligent Dynamic Malware Detection using Machine Learning in IP Reputation for
Forensics Data Analytics. Future Generation Computer Systems118 (2021), 124–141.
Pengbin, F., Jianfeng M., Cong S., Xinpeng X. & Yuwan M. (2018). A Novel Dynamic
Android Malware Detection System with Ensemble Learning. IEEE Access, 6, 30996-
Qiao, Y., Yang, Y., He, J., Tang, C. & Liu, Z. (2014). CBM: free, automatic malware anal- 923
ysis framework using API call sequences. In: Knowledge Engineering and Man- 924
agreement. Springer, Berlin, Heidelberg, 225–236.
Rieck, K., Holz, T., Willems, C., Dussel, P. & Laskov, P. (2008). Learning and classification
of malware behavior, in DIMVA ’08: Proceedings of the 5th international conference
on Detection of Intrusions and Malware, and Vulnerability Assessment. Berlin,
Heidelberg: Springer-Verlag, 108–125.
Souri, A. & Hosseini, R. (2018). A state-of-the-art survey of malware detection approaches
using data mining techniques, Human. Centric. Computing and Information Sciences,
1-22.
Vinayakumar, A., Alazab, M., Soman, M., Poornachandran, P. & Venkatraman, S. (2019).
“Robust Intelligent Malware Detection Using Deep Learning” In IEEE Access, 7,
46717-46738.
Vinayakumar, M., Alazab, K., Soman, P. & Poornachandran, S. (2019). Venkatraman “Robust
Intelligent Malware Detection Using Deep Learning” In IEEE Access, (7), 46717-
Yanfang, Y. (2017). A Survey on Malware Detection Using Data Mining Techniques, ACM
Computing Surveys, 50.

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