EC-Model: An Evolvable Malware Classification Model

Shan Hsin Lee, Shen Chieh Lan, Hsiu Chuan Huang, Chia Wei Hsu, Yung Shiu Chen, Shiuhpyng Shieh

研究成果: Conference contribution同行評審

摘要

Malware evolves quickly as new attack, evasion and mutation techniques are commonly used by hackers to build new malicious malware families. For malware detection and classification, multi-class learning model is one of the most popular machine learning models being used. To recognize malicious programs, multi-class model requires malware types to be predefined as output classes in advance which cannot be dynamically adjusted after the model is trained. When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely. This consumes a significant amount of time and resources, and cannot adapt quickly to meet the timely requirement in dealing with dynamically evolving malware types. To cope with the problem, an evolvable malware classification deep learning model, namely EC-Model, is proposed in this paper which can dynamically adapt to new malware types without the need of fully retraining. Consequently, the reaction time can be significantly reduced to meet the timely requirement of malware classification. To our best knowledge, our work is the first attempt to adopt multi-task, deep learning for evolvable malware classification.

原文English
主出版物標題2021 IEEE Conference on Dependable and Secure Computing, DSC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728175348
DOIs
出版狀態Published - 30 一月 2021
事件2021 IEEE Conference on Dependable and Secure Computing, DSC 2021 - Aizuwakamatsu, Fukushima, Japan
持續時間: 30 一月 20212 二月 2021

出版系列

名字2021 IEEE Conference on Dependable and Secure Computing, DSC 2021

Conference

Conference2021 IEEE Conference on Dependable and Secure Computing, DSC 2021
國家/地區Japan
城市Aizuwakamatsu, Fukushima
期間30/01/212/02/21

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