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To explore the advantages of adversarial learning and deep learning, we propose a novel network intrusion detection model called SAVAER-DNN, which can not only detect known and unknown attacks but also improve the detection rate of low-frequent attacks. SAVAER is a supervised variational auto-encoder with regularization, which uses WGAN-GP instead of the vanilla GAN to learn the latent distribution of the original data. SAVAER's decoder is used to synthesize samples of low-frequent and unknown attacks, thereby increasing the diversity of training samples and balancing the training data set. SAVAER's encoder is used to initialize the weights of the hidden layers of the DNN and explore high-level feature representations of the original samples. The benchmark NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21) and UNSW-NB15 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed SAVAER-DNN is more suitable for data augmentation than the other three well-known data oversampling methods. Moreover, the proposed SAVAER-DNN outperforms eight well-known classification models in detection performance and is more effective in detecting low-frequent and unknown attacks. Furthermore, compared with other state-of-the-art intrusion detection models reported in the IDS literature, the proposed SAVAER-DNN offers better performance in terms of overall accuracy, detection rate, F1 score, and false positive rate.

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Abstract-Achieving fast and precise failure localization has long been a highly desired feature in all-optical mesh networks. M-trail (monitoring trail) has been proposed as the most general monitoring structure for achieving unambiguous failure localization (UFL) of any single link failure while effectively reducing the amount of alarm signals flooding the networks. However, it is critical to come up with a fast and intelligent m-trail design approach for minimizing the number of m-trails and the total bandwidth consumed, which ubiquitously determines the length of the alarm code and bandwidth overhead for the mtrail deployment, respectively. In this paper, the m-trail design problem is investigated. To gain a deeper understanding of the problem, we first conduct a bound analysis on the minimum length of alarm code of each link required for UFL on the most sparse (i.e., ring) and dense (i.e., fully meshed) topologies. Then, a novel algorithm based on random code assignment (RCA) and random code swapping (RCS) is developed for solving the m-trail design problem. The prototype of the algorithm can be found in [1]. The algorithm is verified by comparing to an Integer Linear Program (ILP) approach, and the results demonstrate its superiority in minimizing the fault management cost and bandwidth consumption while achieving significant reduction in computation time. To investigate the impact of topology diversity, extensive simulation is conducted on thousands of random network topologies with systematically increased network density.