Machine Learning for Malicious Traffic Detection in Medical and Health Application Scenarios
GAO Jianyun1,2, LIU Yingying3, DAI Yilan2, LI Shu1
1. Institute of Medical Device Control, China Academy of Food and Drug Control, Beijing 102629, China;
2. School of Medical Devices, Shenyang Pharmaceutical University, Shenyang Liaoning 117004, China;
3. Beijing Medical and Health Technology Development Center, Beijing 100035, China
Abstract：Objective To realize the malicious traffic detection in medical and health application scenarios, the random forest
and decision tree model in machine learning method were used. Methods CIC-ISD2017 sample set were used as the training
and validation set for the model. A total of 1708979 pieces of data were pre-processed in Python for model training. The preprocessed
sample set accounted for 80% of the training set (1367183 pieces) and 20% of the validation set (341795 pieces), and
was trained by adjusting parameters of random forest and decision tree models on sklearn. Finally, 500 network traffic captured
in the built medical and health application scenarios were used as the test set to evaluate the model generalization ability.
Results From the decision tree and random forest confusion matrix, the prediction accuracy of decision tree model for slow
denial-of-service attacks and cross-site scripting attacks was 95%, especially when decision tree model predicted slow denialof-
service attacks, it was confused with cross-site scripting attacks. Random forest model predicted slow denial-of-service
attacks with 99% accuracy, could correctly predict most slow denial-of-service attacks. The random forest model performs well
in medical and health application scenarios. Conclusion The two models achieve ideal results for malicious traffic detection
accuracy in medical and health application scenarios, but the accuracy of the traditional decision tree model is lower than that
of the random forest model. The random forest model is more suitable for malicious traffic detection in medical and health
scenarios, and can provide some reference for future network security research in medical and health application scenarios.
高健云1,2，刘颖颖3，戴依蓝2，李澍1. 机器学习在医疗与健康应用场景下的
恶意流量检测[J]. 中国医疗设备, 2024, 39(1): 12-17.
GAO Jianyun1,2, LIU Yingying3, DAI Yilan2, LI Shu1. Machine Learning for Malicious Traffic Detection in Medical and Health Application Scenarios. China Medical Devices, 2024, 39(1): 12-17.
