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A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph
S. Yang; S. Li; W. Chen; Y. Liu
2020-11-17
Source PublicationIEEE Access   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN2169-3536
Volume8Pages:208120-208135
page numbers16
AbstractThe detection of malware have developed for many years, and the appearance of new machine learning and deep learning techniques have improved the effect of detectors. However, most of current researches have focused on the general features of malware and ignored the development of the malware themselves, so that the features could be useless with the time passed as well as the advance of malware techniques. Besides, the detection methods based on machine learning are mainly static detection and analysis, while the study of real-time detection of malware is relatively rare. In this article, we proposed a new model that could detect malware real-time in principle and learn new features adaptively. Firstly, a new data structure of API-Pair was adopted, and the constructed data was trained with Maximum Entropy model, which could satisfy the goal of weighting and adaptive learning. Then a clustering was practised to filter relatively unrelated and confusing features. Moreover, a detector based on Lont Short Term Memory Network (LSTM) was devised to achieve the goal of real-time detection. Finally, a series of experiments were designed to verify our method. The experimental results showed that our model could obtain the highest accuracy of 99.07% in general tests and keep the accuracies above 97% with the development of malware; the results also proved the feasibility of our model in real-time detection through the simulation experiment, and robustness against a typical adversarial attack.
KeywordMalware detection adaptive learning real-time detection API-pair graph deep learning
PublisherIEEE
DOI10.1109/ACCESS.2020.3038453
Indexed BySCIE
Language英语
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000594430900001
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/441605
Collection兰州大学
信息科学与工程学院
Affiliation
1.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
2.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
3.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
4.Department of Computer Engineering, Santa Clara University, Santa Clara, CA, USA
Recommended Citation
GB/T 7714
S. Yang,S. Li,W. Chen,et al. A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph[J]. IEEE Access,2020,8:208120-208135.
APA S. Yang,S. Li,W. Chen,&Y. Liu.(2020).A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph.IEEE Access,8,208120-208135.
MLA S. Yang,et al."A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph".IEEE Access 8(2020):208120-208135.
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