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.

No abstract

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.

No abstract

Background Due to the multifactorial aetiology and unpredictable long-term stability, skeletal anterior open bite (SAOB) is one of the most intractable conditions for orthodontists. The abnormal orofacial myofunctional status (OMS) may be a major risk factor contributing to the development and relapse of SAOB. This study is aimed at evaluating the OMS and the efficacy of orofacial myofunctional therapy (OMT) alone for SAOB subjects. Methods Eighteen adolescents with SAOB (4 males, 14 females; age: 12–18 years) and eighteen adolescents with normal occlusion (2 males, 16 females; age: 12–18 years) were selected. The electromyographic activity (EMGA) associated with mastication and closed mouth state was measured. Lateral cephalography was used to evaluate craniofacial morphology. Wilcoxon signed rank tests and t-tests were performed to evaluate myofunctional and morphological differences. Pearson or Spearman correlation analysis was used to investigate the correlations between EMGA and morphological characteristics. SAOB subjects were given OMT for 3 months, and the EMGA was compared between before and after OMT. Results During rest, anterior temporalis activity (TAA) and mentalis muscle activity (MEA) increased in SAOB subjects, but TAA and masseter muscle activity (MMA) decreased in the intercuspal position (ICP); and upper orbicularis activity (UOA) and MEA significantly increased during lip sealing and swallowing (P < 0.05). Morphological evaluation revealed increases in the FMA, GoGn-SN, ANS-Me, N-Me, L1-MP, U6-PP, and L6-MP and decreases in the angle of the axis of the upper and lower central incisors and OB in SAOB subjects (P < 0.05). TAA, MMA and anterior digastric activity (DAA) in the ICP were negatively correlated with vertical height and positively correlated to incisor protrusion. MEA was positively correlated with vertical height and negatively correlated with incisor protrusion; and the UOA showed a similar correlation in ICP, during sealing lip and swallowing. After SAOB subjects received OMT, MEA during rest and TAA, MMA and DAA in the ICP increased, while UOA and MEA decreased (P < 0.05). Conclusion SAOB subjects showed abnormal OMS features including aberrant swallowing patterns and weak masticatory muscles, which were interrelated with the craniofacial dysmorphology features including a greater anterior facial height and incisor protrusion. Furthermore, OMT contributes to OMS harmonization, indicating its therapeutic prospect in SAOB.

No abstract

No abstract