| Title | EC-Model: An Evolvable Malware Classification Model |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Lee, Shan-Hsin, Lan, Shen-Chieh, Huang, Hsiu-Chuan, Hsu, Chia-Wei, Chen, Yung-Shiu, Shieh, Shiuhpyng |
| Conference Name | 2021 IEEE Conference on Dependable and Secure Computing (DSC) |
| Keywords | Adaptation models, Deep Learning, Human Behavior, Labeling, Malware, malware classification, malware detection, multi-task learning, Neurons, Predictive Metrics, privacy, pubcrawl, Resiliency, Task Analysis, Training |
| Abstract | 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 When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely.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. |
| DOI | 10.1109/DSC49826.2021.9346248 |
| Citation Key | lee_ec-model_2021 |
EC-Model: An Evolvable Malware Classification Model