| Title | Trojan Traffic Detection Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ma, Zhongrui, Yuanyuan, Huang, Lu, Jiazhong |
| Conference Name | 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) |
| Keywords | Classification algorithms, composability, cyber physical security, cyber physical systems, Data models, feature extraction, machine learning, machine learning algorithms, Network behavior analysis, pubcrawl, random forests, resilience, Resiliency, supply chain security, Traffic analysis, Training, Trojan detection, trojan horse detection, Trojan horses |
| Abstract | 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. |
| DOI | 10.1109/ICCWAMTIP51612.2020.9317515 |
| Citation Key | ma_trojan_2020 |
Trojan Traffic Detection Based on Machine Learning