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IEEE/ICACT20220329 Question.2 Questioner: tomayoon@ieee.org 2022-02-15 오후 12:48:38	IEEE/ICACT20220329 Answer.2 Answer by Auhor anquanip@cnic.cn 2022-02-15 오후 12:48:38
Will you tell us what is the major achievement of this paper, and also what is further study issue from this? Thanks in advance!!	This paper proposed a lightweight intrusion detection model based on feature selection and autoencoder. The model consists of the following three parts: data preprocessing, feature selection and anomaly detection. In the feature selection module, we use a recursive feature addition algorithm, which can select the optimal subset of features and relief the overfitting problem. We use One-Class SVM on the NSL-KDD to verify its effectiveness. In the anomaly detection module, we use the autoencoder, which can adapt to the problem of sample imbalance, so it is suitable for deployment in real network scenarios. Experimental results show that our model can speed up training and detection time, and accurately detect attacks. In the future research, we attempt to use real network traffic data to further evaluate our model.

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