TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Malware Detection with Directed Cyclic Graph and Weight Merging |
| 영문제목(English Title) |
Malware Detection with Directed Cyclic Graph and Weight Merging |
| 저자(Author) |
Eunji Lee
Jikyung Jang
Hyun Kwon
Hyunsoo Yoon
Daeseon Choi
Shanxi Li
Qingguo Zhou
Wei Wei
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| 원문수록처(Citation) |
VOL 15 NO. 09 PP. 3258 ~ 3273 (2021. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware. |
| 키워드(Keyword) |
Big data
QR code
research trend
Text Mining
R program
Network analysis
Data correction
deep neural network
Ensemble Method
Machine Learning
Poisoning attack
Malware detection
directed cyclic graph
Markov chain
Machine Learning
Neural Network
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