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23 August 2022 Linux backdoor detection based on ensemble learning
Yijing Sun, Yihang Li, Chengying Zhu, Yu Chang, Xingyu Bai, Yixin Hong
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Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123300T (2022) https://doi.org/10.1117/12.2646330
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China
Abstract
With the development of the Internet, Linux has received support from software enthusiasts, organizations, and companies all over the world. In addition to maintaining a strong development momentum in servers, it has made great progress in personal computers and embedded systems. But the popularity of Linux has also made it the target of numerous hackers. Linux backdoor not only seriously affects people's daily experience, but also threatens users' property safety, and even directly threatens the normal operation of society. In this paper, the N-Gram model is used for processing, and the TF-IDF algorithm is used to further improve the classification performance, and the ensemble learning algorithm is used to train a high-performance Linux backdoor classification and recognition model. Through experimental analysis, the accuracy of the model is over 97%. Compared with conventional classification models, this model has superior performance.
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Yijing Sun, Yihang Li, Chengying Zhu, Yu Chang, Xingyu Bai, and Yixin Hong "Linux backdoor detection based on ensemble learning", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123300T (23 August 2022); https://doi.org/10.1117/12.2646330
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KEYWORDS
Detection and tracking algorithms
Software development
Feature extraction
Information security
Internet
Machine learning
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