Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree search (MCTS) algorithm. The MCTS algorithm enables the generation model to provide a reward value that reflects the probability of generative examples bypassing the detector. To guarantee the antagonism and feasibility of the generative adversarial examples, the bypassing rules are restricted. The experimental results indicate that the missed detection rate of adversarial examples is significantly improved after the MCTS-T generation algorithm. Additionally, we construct a generative adversarial network (GAN) to optimize the detector and improve the detection rate when dealing with adversarial examples. After several epochs of adversarial training, the accuracy of detecting adversarial examples is significantly improved. INDEX TERMS Network intrusion detection, generative adversarial network, Monte Carlo tree, convolutional neural networks.
Accurate detection of network-based attacks is crucial to prevent security breaches of information systems. The recent application of deep learning approaches for network intrusion detection has shown promising. However, the challenges remain on how to deal with imbalance data and small samples as well as reducing false alarm rate (FAR). To address these issues, this work has proposed a multiple-layer representation learning model for accurate end-to-end network intrusion detection by combining deep convolutional neural networks (CNN) with gcForest. The contributions of this work lie in 1) a new data encoding scheme based on P-Zigzag to encode network traffic data into two-dimensional gray-scale images for representation learning without loss of original information; 2) The combination of gcForest and CNN allows accurate detection on imbalanced data and small scale data with fewer hyperparamters comparing to most existing deep learning models, which increase computational efficiency. The proposed approach is based on a multiple-layer approach consisting of a coarse layer and a fine layer, in which the coarse layer with the improved CNN model (GoogLeNetNP) focuses on identification of N abnormal classes and a normal class. While in the fine layer, an improved model based on gcForest (caXGBoost) further classifies the abnormal classes into N-1 subclasses. This ensures fine-grained detection of various attacks. The proposed framework has been compared with the existing deep learning models using three real datasets (a new dataset NBC, a combination of UNSW-NB15 and CICIDS2017 consisting of 101 classes). The experimental results show that our proposed method outperforms other single deep learning methods (i.e., AlexNet, VGG19, GoogleNet, InceptionV3, ResNet18) in terms of accuracy, detection rate, and FAR, which demonstrates its effectiveness in detecting fine-grained attacks and handling imbalanced datasets with high-precision and low FAR. INDEX TERMS Network intrusion detection, convolutional neural networks, deep random forests, representation learning.
This paper presents a novel 3-D multiregion face recognition algorithm that consists of new geometric summation invariant features and an optimal linear feature fusion method. A summation invariant, which captures local characteristics of a facial surface, is extracted from multiple subregions of a 3-D range image as the discriminative features. Similarity scores between two range images are calculated from the selected subregions. A novel fusion method that is based on a linear discriminant analysis is developed to maximize the verification rate by a weighted combination of these similarity scores. Experiments on the Face Recognition Grand Challenge V2.0 dataset show that this new algorithm improves the recognition performance significantly in the presence of facial expressions.
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