TROJAN TRAFFIC DETECTION BASED ON MACHINE LEARNING

被引：1

作者：

Ma Zhongrui ^{[1
]}

Huang Yuanyuan ^{[1
]}

Lu Jiazhong ^{[1
]}

机构：

[1] Chengdu Univ Informat Technol, Sch Cybersecur, Chengdu 610225, Sichuan, Peoples R China

来源：

2020 17TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP) | 2020年

关键词：

Trojan detection; Traffic analysis; Machine learning; Network behavior analysis;

D O I：

10.1109/ICCWAMTIP51612.2020.9317515

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

At present, most Trojan detection methods are based on the features of host and code. Such methods have certain limitations and lag. This paper analyzes the network behavior features and network traffic of several typical Trojans such as Zeus and Weasel, and proposes a Trojan traffic detection algorithm based on machine learning. First, model different machine learning algorithms and use Random Forest algorithm to extract features for Trojan behavior and communication features. Then identify and detect Trojans' traffic. The accuracy is as high as 95.1%. Comparing the detection of different machine learning algorithms, experiments show that our algorithm has higher accuracy, which is helpful and useful for identifying Trojan.

引用

页码：157 / 160

页数：4

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