Active Learning in Cyberspace Security

Zhang, Pengfei; Liu, Zhenyan; Cui, Jia; Xue, Jingfeng; Cai, Xinfan; Xiao-lei, Yang

doi:10.2991/cimns-18.2018.9

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…In addition, the dataset would be further optimized by capturing the network packet or dataflow [20] to improve the detection performance and a real network intrusion detector would be developed in practical application in the future work.…”

Section: Discussionmentioning

confidence: 99%

ADASYN−Random Forest Based Intrusion Detection Model

Chen

Zhou

2021

2021 4th International Conference on Signal Processing and Machine Learning

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Intrusion detection has been a key topic in the field of cyber security, and the common network threats nowadays have the characteristics of varieties and variation. Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size and difficulty to detect network attacks accurately and efficiently, using Adaptive Synthetic Sampling (ADASYN) method to balance datasets was proposed in this paper. In addition, Random Forest algorithm was used to train intrusion detection classifiers.Through the comparative experiment of Intrusion detection on CICIDS 2017 dataset, it is found that ADASYN with Random Forest performs better. Based on the experimental results, the improvement of precision, recall, F1 scores and AUC values after ADASYN is then analyzed. Experiments show that the proposed method can be applied to intrusion detection with large data, and can effectively improve the classification accuracy of network attack behaviors.Compared with traditional machine learning models, it has better performance, generalization ability and robustness.

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Section: Discussionmentioning

confidence: 99%

ADASYN−Random Forest Based Intrusion Detection Model

Chen

Zhou

2021

2021 4th International Conference on Signal Processing and Machine Learning

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“…Up to now, there have been many studies on traffic classification [13][14][15], which aim at different classification objects and methods as well as output classification results [16][17]. According to the order of occurrence and the adoption of technology, the traffic classification methods can be divided into three stages.…”

Section: Research Progress Of Traffic Classificationmentioning

confidence: 99%