Anomaly Detection Method of SDN Network Edge Switch by ZHAO Yang, YI Peng, ZHANG Zhen, HU Tao, LIU Shaoxun

“…Software-defined network gives programmability to the network,reduces the complexity of network management,and promotes the development of new network technology.As a device for data forwarding and policy enforcement,the permissions of SDN switches should not be stolen by unauthorized entities.However,the SDN switch does not always execute the commands issued by the controller.Malicious attackers attack the network covertly and fatally by eroding the SDN switch,which seriously affects the end-to-end communication quality of users.Communicationsequential process(CSP),as a modeling language designed for concurrent systems,can accurately describe the interaction between SDN switch-switch and switch-host.In this paper,CSP is used to model SDN switch and terminal host,and two abnormal switch location methods are analyzed theoretically.We verify the effectiveness of the two detection methods in the instantiated model system when the edge switch is maliciously forwarded as an egress switch,and the authentication results show that the abnormal behavior cannot be detected.In order to solve this problem,an anomaly detection method for edge switch is proposed in this paper.In this method,the host records the statistical information and triggers the packet_in message to complete the information transmission with the controller by constructing a special packet.The controller collects the statistical information and detects the abnormal forwarding behavior of the edge switch by analyzing the statistical information consistency between the edge switch and the host.Finally,based on the ryu controller,experiments are carried out on the mininet platform,and experimental results show that the edge switch anomaly detection method can successfully detect abnormal behavior.…”
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Time Series Anomaly Detection Model with Dual Attention Mechanism by YANG Chaocheng, YAN Xuanhui, CHEN Rongjun, LI Hanzhang

Subjects: “…time series; anomaly detection; deep learning; attention; autoencoder…”
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Network Equipment Anomaly Detection Based on Time Delay Feature by CUI Jingsong, ZHANG Tongtong, GUO Chi, GUO Wenfei

Subjects: “…anomaly detection|delay|network equipment|one-class support vector machine|peak position…”
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Semi-supervised Network Traffic Anomaly Detection Method Based on GRU by LI Haitao, WANG Ruimin, DONG Weiyu, JIANG Liehui

“…Intrusion detection system(IDS) is a detection system that can issue an alarm when a network attack occurs.Detecting unknown attacks in the network is a challenge that IDS faces.Deep learning technology plays an important role in network traffic anomaly detection,but most of the existing methods have a high false positive rate and most of the models are trained using supervised learning methods.A gated recurrent unit network(GRU)-based semi-supervised network traffic anomaly detection me-thod(SEMI-GRU) is proposed,which combines a multi-layer bidirectional gated recurrent unit neural network(MLB-GRU) and an improved feedforward neural network(FNN).Data oversampling technology and semi-supervised learning training method are used to test the effect of network traffic anomaly detection using binary classification and multi-classification methods,and NSL-KDD,UNSW-NB15 and CIC-Bell-DNS-EXF-2021 datasets are used for verification.Compared with classic machine learning mo-dels and deep learning models such as DNN and ANN,the SEMI-GRU method outperforms the machines lear-ning and deep learning methods listed in this paper in terms of accuracy,precision,recall,false positives,and F1 scores.In the NSL-KDD binary and multi-class tasks,SEMI-GRU outperforms other methods on the F1 score metric,which is 93.08% and 82.15%,respectively.In the UNSW-NB15 binary and multi-class tasks,SEMI-GRU outperforms the other methods on the F1 score,which is 88.13% and 75.24%,respectively.In the CIC-Bell-DNS-EXF-2021 light file attack dataset binary classification task,all test data are classified correctly.…”
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Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns by WEI Hui, CHEN Ze-mao, ZHANG Li-qiang

“…The existing system call-based anomaly intrusion detection methods can’t accurately describe the behavior of the process by a single trace pattern.In this paper,the process behavior is modeled based on the sequence and frequency patterns of system call trace,and a data-driven anomaly detection framework is designed.The framework could detect both sequential and quantitative anomalies of the system call trace simultaneously.With the help of combinational window mechanism,the framework could realize offline fine-grained learning and online anomaly real-time detection by meeting different requirements of offline trai-ning and online detection for extracting trace information.Performance comparison experiments of unknown anomalies detection are conducted on the ADFA-LD intrusion detection standard dataset.The results show that,compared with the four traditional machine learning methods and four deep learning methods,the comprehensive detection performance of the framework improves by about 10%.…”
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Study on Air Traffic Flow Recognition and Anomaly Detection Based on Deep Clustering by RAO Dan, SHI Hongwei

Subjects: “…trajectory clustering|anomaly detection|deep neural network|autoencoder|ads-b…”
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A steam turbine anomaly detection method based on O-DAE and SVDD by XU Weimin, LI Xuemin, ZHANG Yi, Maulidi Barasa, ZHANG Peize, YI Youzhong

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Anomaly detection of power load data based on Transformer and generative adversarial networks by LU Danhong, FAN Wenyao, YANG Ting, NI Minjue, LI Siqi, ZHU Xiao

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Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance by XU Tian-hui, GUO Qiang, ZHANG Cai-ming

Subjects: “…anomaly detection|probability density ratio|time delay|total variation|relative total variation…”
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Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network by WANG Xin-tong, WANG Xuan, SUN Zhi-xin

Subjects: “…network traffic anomaly detection|multi-scale memory residual network|multi-scale one-dimensional convolution|long short-term memory network|residual network|network intrusion detection…”
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Adaptive multi-layer structure with spatial-spectrum combination for hyperspectral image anomaly detection

“…A new algorithm for hyperspectral image anomaly detection is proposed by designing an adaptive multi-layer structure with spatial-spectral combination information, which is different from the traditional anomaly detection algorithms only considering the spectral difference between the anomaly point and the background pixels, and ignoring the difference between the local spatial structure and spectrum. …”
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Study on Anomaly Detection and Real-time Reliability Evaluation of Complex Component System Based on Log of Cloud Platform by WANG Bo, HUA Qing-yi, SHU Xin-feng

Subjects: “…log parsing|anomaly detection|reliability evaluation|root cause analysis|ensemble learning|complex components…”
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Unsupervised abnormal track detection method based on GRU-VAE by LI Lei, ZHANG Jing, OUYANG Qicheng, ZHOU Mingkang

Subjects: “…data mining|track data|anomaly detection|unsupervised learning…”
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Application of Transformer in anomaly indicators forecasting of hydropower units by LIN Yemin, WANG Ning, QIU Rongjie, TANG Yuchao, ZHOU Guanqun, LI Zezhou, WANG Zhongya

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Abnormal Detection Method of Tool Machining Monitoring Data Based on Self-Paced Learning by ZHANG Jian, HU Xiaofeng, ZHANG Yahui

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Outlier detection method for geotechnical engineering based on MetaOD model selection by Tongtong Zou, Xiaoyi Liu, Jinquan Liu, Hailiang Yuan, Yubin Lu, Wanhu Zhang

Subjects: “…geotechnical engineering anomaly detection…”
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Research on optimization selection of anti-submarine aircraft course in aero-magnetic exploration by SHAN Zhi-chao, ZHENG Xiao-qing, LI Da-wei, LU Ren-wei

Subjects: “…aviation anti-submarine|magnetic anomaly detection|aeromagentic detection|optimal heading…”
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Dataflow Feature Analysis for Industrial Networks Communication Security

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Detection method of wind speed anomaly fluctuation based on SSA−LSTM by Lijun DENG, Jinbo YUAN, Jian LIU, Wentian SHANG

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Characteristics of Soil Geochemistry and Its Prospecting Significance in Liemen Area, Jinchuan County, Sichuan by Wei Luo, Xiangyuan Yue, Xiong Zhou, Jing Peng, Wenwen Chao, Wanjun Huan, Wei Liu

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