With the rapid development of the Internet, various types of anomaly traffic are threatening network security. We consider the problem of anomaly network traffic detection and propose a three-stage anomaly detection framework using only normal traffic. Our framework can generate pseudo anomaly samples without prior knowledge of anomalies to achieve the detection of anomaly data. Firstly, we employ a reconstruction method to learn the deep representation of normal samples. Secondly, these representations are normalized to a standard normal distribution using a bidirectional flow module. To simulate anomaly samples, we add noises to the normalized representations which are then passed through the generation direction of the bidirectional flow module. Finally, a simple classifier is trained to differentiate the normal samples and pseudo anomaly samples in the latent space. During inference, our framework requires only two modules to detect anomalous samples, leading to a considerable reduction in model size. According to the experiments, our method achieves the state of-the-art results on the common benchmarking datasets of anomaly network traffic detection. The code is given in the https://github.com/ZxuanDang/ATD-via-Flows.git
TrajFlow: A Generative Framework for Occupancy Density Estimation Using Normalizing Flows
Department of Computer Science University of Minnesota（计算机科学系明尼苏达大学）；Department of Civil Environmental and Geo- Engineering University of Minnesota（土木环境与地球工程系明尼苏达大学）
Discrete Flows: Invertible Generative Models of Discrete Data
Google Brain（谷歌大脑）；Columbia University（哥伦比亚大学）
虽然规范化流在建模高维连续分布方面取得了重大进展，但它们对离散分布的适用性仍然未知。在本文中，我们表明，流实际上可以扩展到离散事件-并根据一个简单的变量变化公式，不需要对数行列式雅可比计算。离散流有许多应用。我们考虑两个流架构：离散自回归流，使双向性，允许，例如，在文本中的令牌依赖于从左到右和从右到左的上下文中的一个确切的语言模型;和离散二分流，使高效的非自回归生成在RealNVP。从经验上讲，我们发现离散自回归流在合成离散分布、加法任务和Potts模型上的表现优于自回归基线;而二分流在Penn Tree Bank和text 8的字符级语言建模上可以获得与自回归基线竞争的性能。
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Florida（佛罗里达大学）；Wuhan University（武汉大学）；Duke Kunshan University（昆山杜克大学）
DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining
Beihang University（北京航空航天大学）；National Computer Network Emergency Response Technical Team/Coordination Center of China（国家计算机网络应急技术处理协调中心）；Beijing University of Posts and Telecommunications（北京邮电大学）
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
Zhejiang University of Technology（浙江工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；Institute of Cyberspace Security（网络空间安全研究院）
Regression prediction algorithm for energy consumption regression in cloud computing based on horned lizard algorithm optimised convolutional neural network-bidirectional gated recurrent unit
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The University of Tokyo（东京大学）；Georgia Institute of Technology（佐治亚理工学院）；Northeastern University（东北大学）
CST-AFNet: A dual attention-based deep learning framework for intrusion detection in IoT networks
Skill Morph；Bangladesh Army University of Science & Technology（孟加拉国陆军科技大学）；Daffodil International University（达福迪尔国际大学）；State University of Bangladesh（孟加拉国州立大学）
