CLC number: TP309.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-09-15
Cited: 0
Clicked: 7935
Liu Liu, Bao-sheng Wang, Bo Yu, Qiu-xi Zhong. Automatic malware classification and new malware detection using machine learning[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1336-1347.
@article{title="Automatic malware classification and new malware detection using machine learning",
author="Liu Liu, Bao-sheng Wang, Bo Yu, Qiu-xi Zhong",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="9",
pages="1336-1347",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601325"
}
%0 Journal Article
%T Automatic malware classification and new malware detection using machine learning
%A Liu Liu
%A Bao-sheng Wang
%A Bo Yu
%A Qiu-xi Zhong
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 9
%P 1336-1347
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601325
TY - JOUR
T1 - Automatic malware classification and new malware detection using machine learning
A1 - Liu Liu
A1 - Bao-sheng Wang
A1 - Bo Yu
A1 - Qiu-xi Zhong
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 9
SP - 1336
EP - 1347
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601325
Abstract: The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.
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