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arXiv 2019年

作者： Chen, Shichuan Zheng, Shilian Yang, Lifeng Yang, Xiaoniu Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used is very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900,000 and 13,000,000, respectively, and with categories of 3,143 and 5,157 respectively. We use the same Inception-Residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. Experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： White noise

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SigNet: A novel deep learning framework for radio signal classification

arXiv

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arXiv 2020年

作者： Chen, Zhuangzhi Cui, Hui Xiang, Jingyang Qiu, Kunfeng Huang, Liang Zheng, Shilian Chen, Shichuan Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Utron Technology Co. Ltd. [as Hangzhou Qianjiang Distinguished Expert Hangzhou310056 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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An Optimized Method of Packet Loss Rate under Backup Path Protection in OBS Network

引用

Journal of Physics: Conference Series 2021年第1期1746卷

作者： Aodong Meng Yucheng Chen Jiahao Dai Yongqun Chen Science and Technology on Communication Information Security Control Laboratory Jiaxing China No.36 Research Institute of CETC Jiaxing China

In order to calculate the packet loss rate in OBS network, this paper proposes a method, which performs good in both accuracy and rapidity, based on Erlang B formula to estimate the packet loss probability in OBS network using backup path protection mechanism. This method can be used to optimize the design of OBS network protocol, and it also provides an accurate and efficient approximate calculation method of packet loss rate in OBS network and can be conductive to the study of using copy transmission to enhance the network survivability. Based on the formula of Erlang B, this paper briefly introduces the derivation process and principle of this method. The basic experimental principles of simulation and estimation processes are designed in this paper, and the experimental results of the proposed method under a typical network topology (ARPA network) with no protection mechanism and 1 and 2 backup paths protection mechanisms are analyzed to verify its rapidity and accuracy. The results of experiments show the accuracy and efficiency of this method.

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Nth-order nonlinear intensity fluctuation amplifier

arXiv

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arXiv 2021年

作者： Zhang, Shuanghao Zheng, Huaibin Wang, Gao Chen, Hui Liu, Jianbin Zhou, Yu He, Yuchen Luo, Sheng Liu, Yanyan Xu, Zhuo Key Laboratory of Multifunctional Materials and Structures Ministry of Education School of Electronic Science and Engineering Xi'An Jiaotong University Shaanxi Xi'an710049 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China School of Physics & Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Department of Applied Physics Xi'An Jiaotong University Xi'an710049 China

Stronger light intensity uctuations are pursued by related applications such as optical resolution, image enhancement, and beam positioning. In this paper, an Nth-order light intensity uctuation amplifier is proposed, which was demonstrated by a four-wave mixing process with different statistical distribution coupling lights. Firstly, its amplification mechanism is revealed both theoretically and experimentally. The ratio R of statistical distributions and the degree of second-order coherence g(2)(0) of beams are used to characterize the affected modulations and the increased light intensity uctuations through the four-wave mixing process. The results show that the amplification of light intensity uctuations is caused by not only the uctuating light fields of incident coupling beams, but also the uctuating nonlinear coefficient of interaction. At last, we highlight the potentiality of applying such amplifier to other N-order nonlinear optical effects. Copyright © 2021, The Authors. All rights reserved.

关键词： Image enhancement

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Simulation Environment of Evaluation and Optimization for Moving Target Defense: A SimPy Approach 2019

Simulation Environment of Evaluation and Optimization for Mo...

引用

Proceedings of the 2019 the 9th International Conference on Communication and Network security

作者： Xinli Xiong Linru Ma Chao Cui Army Engineering University of People's Liberation Army Nanjing Jiangsu China National Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9781450376624

In this paper, a simulation environment for evaluating the effectiveness and performance cost of moving target defense (MTD) is presented. This simulation environment is based on SimPy, which is a process-based discrete-event simulation framework worked on standard Python. In our MTD simulation environment, we develop a typical topology to contain different deployments of moving target (MT) techniques. Meanwhile, processes of attackers, defenders, services, and users are analyzed and modeled in the simulation environment, which also provides an extendable interface for MT technologies in the future. In the case study, we quantify the effectiveness and performance of MTD in the proposed simulation environment under several typical scenarios, none-MTD deployment, high-security strategy, high-performance strategy. The average attacking time, average processed requests, and average request's processing time are used as metrics to assess different MTD strategies. Results show that our simulation environment provides an efficient and intuitionistic method for evaluating the effectiveness and performance of MTD and helping to choose a more optimal strategy set for MTD.

关键词： Discrete event simulation Performance quantification Moving target defense SimPy

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Simulation environment of evaluation and optimization for moving target defense: A SiMpy approach 9

Simulation environment of evaluation and optimization for mo...

引用

9th International Conference on Communication and Network security, ICCNS 2019

作者： Xiong, Xinli Ma, Linru Cui, Chao Army Engineering University of People's Liberation Army Nanjing Jiangsu China National Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9781450376624

In this paper, a simulation environment for evaluating the effectiveness and performance cost of moving target defense (MTD) is presented. This simulation environment is based on SimPy, which is a process-based discrete-event simulation framework worked on standard Python. In our MTD simulation environment, we develop a typical topology to contain different deployments of moving target (MT) techniques. Meanwhile, processes of attackers, defenders, services, and users are analyzed and modeled in the simulation environment, which also provides an extendable interface for MT technologies in the future. In the case study, we quantify the effectiveness and performance of MTD in the proposed simulation environment under several typical scenarios, none-MTD deployment, high-security strategy, high-performance strategy. The average attacking time, average processed requests, and average request’s processing time are used as metrics to assess different MTD strategies. Results show that our simulation environment provides an efficient and intuitionistic method for evaluating the effectiveness and performance of MTD and helping to choose a more optimal strategy set for MTD. © 2019 Association for Computing Machinery.

关键词： Discrete event simulation

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A security scheme for intelligent substation communications considering real-time performance

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Journal of Modern Power Systems and Clean Energy 2019年第4期7卷 948-961页

作者： Jie ZHANG Jun’e LI Xiong CHEN Ming NI Ting WANG Jianbo LUO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China NARI Technology Co.Ltd. Nanjing 211106China State Key Laboratory of Smart Grid Protection and Control Nanjing 211106China

Tampering,forgery and theft of the measurement and control messages in a smart grid could cause one breakdown in the power ***,no security measures are employed for communications in intelligent *** services in an intelligent substation have high demands for real-time performance,which must be considered when deploying security *** paper studies the security requirements of communication services in intelligent substations,analyzes the security capabilities and shortages of IEC 62351,and proposes a novel security scheme for intelligent substation *** security scheme covers internal and telecontrol communications,in which the real-time performance of each security measure is *** this scheme,certificateless public key cryptography(CLPKC)is used to avoid the latency of certificate exchange in certificate-based cryptosystem and the problem of key escrow in identity-based cryptosystem;the security measures of generic object-oriented substation event,sampled measure value and manufacturing message specification in IEC 62351 are improved to meet the real-time requirements of the messages as well as to provide new security features to resist repudiation and replay attacks;and the security at transport layer is modified to fit CLPKC,which implements mutual authentication by exchanging ***,a deployment of CLPKC in an intelligent substation is *** also evaluate the security properties of the scheme and analyze the end-to-end delays of secured services by combining theoretical calculation and simulation in this *** results indicate that the proposed scheme meets the requirements of security and real-time performance of communications in intelligent substations.

关键词： Intelligent substation security measures Certificateless public key cryptography(CLPKC) Real-time communication IEC 62351

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LSTM-based Frequency Hopping Sequence Prediction

LSTM-based Frequency Hopping Sequence Prediction

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Gao Li Jianliang Xu Weiguo Shen Wei Wang Zitong Liu Guoru Ding Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China College of Communications Engineering Army Engineering University China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction.

关键词： Predictive models Time-frequency analysis Frequency control Training Time series analysis Logic gates Spread spectrum communication

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Congestion control of Multi-layer Cellular Networks based on Modeling of Transmit Power

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Journal of Physics: Conference Series 2021年第1期1746卷

作者： Jiahao Dai Yongqun Chen Yucheng Chen Aodong Meng Science and Technology on Communication Information Security Control Laboratory China Electronic Group Corporation NO.36 Institute Zhejiang Jiaxing China

The explosive growth of network traffic and the limited storage node resources make the existing network architecture bear great pressure, and network congestion appears in work and life from time to time. In order to ensure the timeliness and effectiveness of the network, we propose a congestion control method based on transmission power. Different from previous work, we built a multi-layer cellular network consisting of multiple macro stations, small base stations and mobile terminals. On this basis, we establish a congestion control model based on the mixed queuing model of service window and calculate the input/output rate and congestion. Finally, we use simulation to verify the effectiveness of the congestion model. The network builder can use our model to set the appropriate power of macro stations and small base stations to ensure network stability.

关键词：

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Gas sensor fault diagnosis based on Convolutional Neural Network

Gas sensor fault diagnosis based on Convolutional Neural Net...

引用

第三届材料科学应用与能源材料国际研讨会

作者： Yongyi Sun Yangyang Liu Fang Ji Gang Li Yanjun Ma Jin Li Lixin Yang Hongquan Zhang Key Laboratory of Electronics Engineering College of Heilongjiang Province Heilongjiang University Department of Public security information Liaoning Police Collage School of Information Technology Guangxi Police College School of electrical and control engineering Heilongjiang University of Science & Technology School of Automation Harbin Engineering University

Gas sensor often fails due to the influence of temperature, humidity, lighting, dust and toxic gases, and causes unreliable phenomena. Therefore, the fault diagnosis of gas sensors is considered as a weak link. Feature extraction and classification play an important role in gas sensor fault diagnosis. However, there are many problems in traditional feature extraction methods. For example, 1) requirements for expert experience, 2) sensitivity of changes in mechanical systems, 3) limitations of new feature extraction. Therefore, it is meaningful and attractive to develop a method that can discover and learn fault-sensitive features of gas sensors from the original data and classify them effectively according to the sensitive features. Convolutional neural network(CNN) has been widely applied in image analysis and voice recording, and has achieved great success. However, gas sensor fault diagnosis is rarely applied. This paper focuses on using CNN to learn fault features from data, extract features automatically, and then classify them effectively. Experiments show that the CNN method provides a effective solution for gas sensor fault diagnosis.

关键词： Gas sensor fault diagnosis based on Convolutional Neural Network

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