ItemConference Paper

Static Analysis and Machine Learning for Runtime Library Detection in Linux Binaries

Title
Static Analysis and Machine Learning for Runtime Library Detection in Linux Binaries
Creator
Anu, P.; Saseekala, M.; Mohanapriya, D.; Thamaraikannan, N.; Ponmozhi, K.
Description
The upsurge of malware targeting Internet of Things (IoT) devices demands effective approaches. This work announces a new method, stimulated by MANTILLA, which influences machine learning models. Through a prominence on architecture-independent characteristics from binary procedures, the system progresses its competence to differentiate among several libraries as well as architectures. Classification accuracy is further enhanced by employing a majority voting technique such that the output of the model is robust and reliable. Besides the machine learning-based classification, the paper incorporates a malware detection module based on signature matching. This two-pronged approach enables the system to cross-check discovered runtime libraries against a large database of pre-collected malware signatures. By marking possible security threats according to this comparison, the system greatly increases its ability to identify malicious binaries, thus offering an added layer of security for IoT devices. This unification of detection and classification mechanisms plays an important role in dealing with the changing nature of malware threats. Although encouraging results were obtained through this project, more evaluation should be done for comparison of the efficiency of KNN with other models, for example, Random Forest. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Source
Communications in Computer and Information Science;Volume;2845 CCIS;pp.393-406
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Binary code analysis; Malware; Runtime library identification; Statically linked binaries
Coverage
Anu P., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India; Saseekala M., Faculty of Computer Applications, School of Business and Management, CHRIST University, Bangalore, India; Mohanapriya D., Department of Computer Science, PSG College of Arts and Science, Coimbatore, 641014, India; Thamaraikannan N., Department of Artificial Intelligence and Machine Learning, Kongunadu Arts and Science College, Coimbatore, India; Ponmozhi K., Department of Computer Applications, SRM Valliammai Engineering College, Kattankulathur, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18650929; ISBN: 978-303220906-1;
Format
online
Language
English
Type
Conference paper

Citation

Anu, P.; Saseekala, M.; Mohanapriya, D.; Thamaraikannan, N.; Ponmozhi, K., “Static Analysis and Machine Learning for Runtime Library Detection in Linux Binaries,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25409.

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