A Feedback Semi-Supervised Learning With Meta-Gradient for Intrusion Detection

The widespread popularity of the Internet of human life has been accompanied by a significant increase in the cost of protecting private data from malicious attacks. Researchers have proposed many deep learning-based intrusion detection methods. However, traditional methods rely on a large number of unpolluted data to learn benign data distributions, while nonidentical distributions will affect the performance in distinguishing normal and abnormal data. To address this problem, this article proposes an intrusion detection method feedback semi-supervised learning with meta-gradient for intrusion detection (FSMG) based on feedback deep semi-supervised learning. FSMG constructs a lightweight evaluation network with slight data augmentation and nonprocessing on the same input, respectively, and uses a small amount of labeled data to infer nonidentically distributed data flows hidden in the training dataset. Then, FSMG converts malicious flows into useful information, continuously track and update the model, reducing data labeling errors. Furthermore, for the updating of gradients in the model, a bilevel nested optimization is used to ensure the model converges within O(C/root T). Unlike other semisupervised algorithms, FSMG uses labeled data and nonidentically distributed unlabeled data proportionally to construct the training dataset, achieving higher classification accuracy, and better robustness even with an 80% polluted rate.