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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Di Chen Gang Xiong Baigang Huang Lijun Wang Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of Cetc Jiaxing China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Institute of Navigation and Sensing Shanghai Jiao Tong University China

ISBN: (纸本)9781665446006

In this paper, a modulated signal recognition method based on feature fusion and ResCNN (FF-ResCNN) under generalized fractal noise background is proposed. Firstly, in the feature extraction and fusion part, considering the robustness, anti-noise and interpretability of the features, the fusion feature vectors of generalized fractal spectrum, instantaneous feature and high-order cumulant feature are constructed. Secondly, in order to suppress the possible gradient and degradation problems, the ResCNN model is used to train and classify the fusion features when the CNN model is used to further collect the deep-level abstract features. Finally, feature extraction, feature fusion and classification experiments are carried out for nine modulated signals under two noise types (GWN and fractal noise). Experimental results show that in the case of generalized fractal noise, when SNR=0dB, the recognition accuracy of signal modulation type reaches 95.42%, which verifies the superiority of the proposed method. At the same time, the simulation analysis is carried out for different feature fusion schemes, which provides a valuable reference for feature combination optimization under different noise environment.

关键词： Degradation Deep learning Conferences Neural networks Modulation Feature extraction Fractals

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A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

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Computers, Materials & Continua 2020年第5期63卷 705-724页

作者： Qiang Wei Guangmin Hu Chao Shen Yunfei Yin School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an710049China The Electronic Engineering Department Universidad de SevillaSeville41004Spain School of Astronautics Harbin Institute of TechnologyHarbin150001China

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and ***,the existing methods are time-consuming for even moderate-scale *** this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance *** effectiveness of the proposed method is validated through synthetic and real-world *** experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

关键词： Network discovery shortest-path cover shortest-path distance query large complex networks

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An Indoor Wi-Fi Positioning Approach optimized by Virtual Node

An Indoor Wi-Fi Positioning Approach optimized by Virtual No...

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International Conference on Computers, communications, and Systems (ICCCS)

作者： Jie-tai Wang Xiao-niu Yang Zhen-xing Luo School of Mathematics and Information Engineering Jiaxing University Jiaxing China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

In the indoor positioning application based on the Wi-Fi range-based positioning algorithm, on the one hand, due to the characteristics of the wireless propagation model, the final position of the node calculated by the range-based positioning algorithm will have a certain error, on the other hand, due to the complexity of the wireless AP deployment, it will cause some regional deployments may not meet the requirements of trilateral positioning. In response to these problems, this paper proposed an optimized Wi-Fi positioning algorithm based on virtual node optimization. On the one hand, a wireless AP with a known physical location is used as a virtual positioning node to correct the positioning results; on the other hand, a virtual AP mechanism is designed to compensate for this. Due to the problem of positioning blind spots caused by AP deployment defects, the accuracy of the Wi-Fi range-based positioning algorithm is greatly improved, and the effectiveness of the algorithm is verified through simulation verification and actual site operation tests.

关键词： Wireless communication Wireless fidelity Fingerprint recognition Distance measurement Meters Floors communication system security

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

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A Honeypot-enabled SDN-based Selector for Industrial Device Access control

A Honeypot-enabled SDN-based Selector for Industrial Device ...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Jun Li Chunhui Wang Zhenyu Li Yong Ding Cyber Emergency Response Team CHINA Industrial Control Systems Beijing China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Cryptography and Information Security Guilin China

ISBN: (纸本)9781665418164

At present, there are a large number of cyber-attacks in global networks, which pose a serious threat to critical infrastructure and cause a large number of potential security risks. In this paper, we discussed the hybrid application of software-defined network technology and honeypot technology to protect critical infrastructure, which can defend against attacks such as advanced persistent threats (APT). Therefore, we propose a honeypot-enable selector based on software defined networking (SDN) technology, which can establish an authentication link through a special packet to achieve access control. Besides, the selector can implement protection without changing the original network topology. Finally, we evaluate our proposed selector through experiments. Experimental results show that the proposed selector can divert packets from non-authenticated sources to the honeypot, which can protect actual devices. Furthermore, the experimental results show that the proposed selector has good forwarding performance.

关键词： Access control Performance evaluation Network topology Authentication Cyberspace Switches Data science

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SFNet: Stage Spatial Attention and Feature Waterfall Fusion for Mathematical Document Text Line Detection

SFNet: Stage Spatial Attention and Feature Waterfall Fusion ...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Chang Liu Jiaxin Liu Yong Zhang Lei Huang University of Electronic Science and Technology of China Chengdu Sichuan China Science and Technology on Electro-Optical Information Security Control LaboratoryTianjinChina300308 China Sichuan College of Architectural Technology Deyang Sichuan China

ISBN: (纸本)9781665421751

Text line detection is an important technique in layout analysis. Due to the existence of non-text areas and the change of formula height, text line detection in mathematical documents has always been a huge challenge. In view of the complexity of the spatial structure of mathematical text lines, on the one hand, based on the existing attention mechanism, we proposed a composite network structure with Stage Spatial Attention, which has better feature extraction. In addition, a multi-scale feature fusion structure, called Feature Waterfall Fusion, adds features with low-resolution but stronger semantic information into the features with high-resolution but weaker semantic information indirectly and directly. Combining them results in SFNet. On the other hand, to explore the mathematical document text line detection, we constructed a dataset named Mathematical Document Text Line Dataset (MDTL-1850), which includes about 10,669 text line annotations in 1,850 images (1,280 for training and 570 for testing). We assembled SFNet and a learnable post-processing Progressive Scale Expansion (PSE) surpassed mainstream text detection networks such as PSENet and PANet in all metrics on MDTL-1850. Precision $(P)$ , Recall $(R)$ and $F_information$ , which improved by 1.0, 0.7 and 0.9 respectively. The experimental results strongly proved the superiority of SFNet network in detecting text line of mathematical documents.

关键词： Training Measurement Semantics Layout Feature extraction Software Robustness

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M-to-N Backdoor Paradigm: A Multi-Trigger and Multi-Target Attack to Deep Learning Models

arXiv

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arXiv 2022年

作者： Hou, Linshan Hua, Zhongyun Li, Yuhong Zheng, Yifeng Zhang, Leo Yu School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong Shenzhen518055 China Xiaohongshu Technology Co. Ltd. Shanghai China School of Information and Communication Technology Griffith University Gold Coast SouthportQLD4215 Australia

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where a backdoored model behaves normally with clean inputs but exhibits attacker-specified behaviors upon the inputs containing triggers. Most previous backdoor attacks mainly focus on either the all-to-one or all-to-all paradigm, allowing attackers to manipulate an input to attack a single target class. Besides, the two paradigms rely on a single trigger for backdoor activation, rendering attacks ineffective if the trigger is destroyed. In light of the above, we propose a new M-to-N attack paradigm that allows an attacker to manipulate any input to attack N target classes, and each backdoor of the N target classes can be activated by any one of its M triggers. Our attack selects M clean images from each target class as triggers and leverages our proposed poisoned image generation framework to inject the triggers into clean images invisibly. By using triggers with the same distribution as clean training images, the targeted DNN models can generalize to the triggers during training, thereby enhancing the effectiveness of our attack on multiple target classes. Extensive experimental results demonstrate that our new backdoor attack is highly effective in attacking multiple target classes and robust against pre-processing operations and existing defenses. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Accelerating NATO Transformation with SnTEE: Experiments with Network security Function Virtualization in Coalition Networks

Accelerating NATO Transformation with SnTEE: Experiments wit...

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International Conference on Military communications and information Systems (ICMCIS)

作者： Roberto Rigolin F. Lopes Johannes F. Loevenich Konrad Wrona Paulo H. L. Rettore Jerry Falkcrona Joseph Mathews Nils Nordbotten Bogdan Vasilache Thorsten Lampe Olwen L. Worthington Juha Röning Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany Department of Mathematics/Computer Science University of Osnabrück Osnabrück Germany NATO Cyber Security Centre The Hague Netherlands Military University of Technology Warsaw Poland Communication Systems (KOM) Fraunhofer FKIE Bonn Germany Cyber Defence and C2 Technology Swedish Defence Research Agency Linköping Sweden Information Technology Division U.S. Naval Research Laboratory Washington DC USA Thales Norway Oslo Norway WonderBit B.V. Almere The Netherlands Defence Science and Technology Laboratory (Dstl) UK Ministry of Defence London United Kingdom Information Technology and Electrical Engineering University of Oulu Oulu Finland

This paper introduces a virtual laboratory for experiments with cybersecurity functions in coalition networks. The motivation comes from the NATO Allied Command Transformation (ACT) initiative on science and technology Experimentation Environment (SnTEE), which supports the transfer of scientific and technical activities carried out by the NATO science and technology Organization (STO) research task groups towards NATO operations. The goal of the experiments reported in this paper is to emulate a network topology inspired by Protected Core Networking (PCN), which defines two interfaces. One interface between two protected core segments, from different nations, and another interface between a protected core segment and a colored cloud within a particular nation. The experiments are intended to support the investigation performed by the STO IST-196 RTG on Cyber security in Virtualized Networks. In particular, the starting hypothesis of IST-196 is that security function virtualization can support/speedup PCN topology changes when links are added/removed. We define our experimental setup as Infrastructure as Code (IaC) and introduce experiments that emulate topology changes. We also sketch a solution for the deployment of two chains of virtualized cybersecurity functions at lower protocol layers (physical and IP) and at higher protocol layers (transport and application). This paper also lists future experiments for the NATO SnTEE lab, namely risk-aware routing, cross-layer enforcement of policies, information exchange functions, and federate monitoring and detection of cyber incidents/attacks.

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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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science China(information sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Channel Estimation Based on Lorentzian Adaptive Algorithm in a-stable Noise Environment 9

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： Cherednichenko, Aleksey Huang, Xinqi Wang, Yanyan Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781728198057

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel ina-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons. © 2020 IEEE.

关键词： Channel estimation

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