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17th International Conference on Computational Intelligence and security, CIS 2021

作者： Dai, Jiahao Chen, Yongqun China Electronic Corporation NO.36 Institute Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665494892

This paper mainly studies an attack effectiveness evaluation method for AD hoc networks based on rough set theory. Firstly, we use OPNET to build AD hoc network simulation scenario, design and develop attack module, and obtain network performance parameters before and after the attack. Then the rough set theory is used to evaluate the attack effectiveness. The results show that this method can effectively evaluate the performance of AD hoc networks before and after attacks. © 2021 IEEE.

关键词： Rough set theory

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An analysis of network traffic identification based on decision tree 2

An analysis of network traffic identification based on decis...

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2nd International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2021

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

ISBN: (纸本)9781665432658

With the strong development momentum of the Internet, network not only brings convenience to our life, but also poses threats to our network security. Users can hide their IP and identity information through encryption algorithm, and carry out various illegal activities and transactions in the network. Therefore, it will be of great value to identify special traffic which impact on network security and exploring abnormal situations in the network. Special traffic is important to the security of the network space. In this paper, we establish a decision tree model based on C4.5 and random forest to compare the accuracy of the two decision tree algorithms in traffic classification. Experimental results show that these two methods can identify traffic effectively. © 2021 IEEE.

关键词： Decision trees

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Research on Multi - Target Data Association and Location Algorithm Based on Passive Multi - Sensor System 18

Research on Multi - Target Data Association and Location Alg...

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18th International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2021

作者： Siyi, Yao Jianliang, Xu Weiguo, Shen Li, Wang Wanchun, Li School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

ISBN: (纸本)9781665413640

In this paper, the multi-target positioning research is carried out on the basis of a passive multi-sensor reconnaissance system, which requires an algorithm to solve the problem of multi-target data association and positioning in the presence of false alarms and missed detections (also named non-ideal). This paper proposes a new data association and localization algorithm. This new algorithm adopts the method of hierarchical association of data, achieves data association through coarse association and fine association, and finally deletes redundant targets through backtracking. The simulation results show the effectiveness and stability of the algorithm. © 2021 IEEE.

关键词： Data association Multi-target Non-ideal Passive multi-sensor

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期PP卷 PP页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Image-Text Matching Medical Report Generation Sample-graph Consistency Self-boosting framework

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Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision

arXiv

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arXiv 2024年

作者： Jin, Chengxiang Zhou, Jiajun Xie, Chenxuan Yu, Shanqing Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Binjiang Institute of Artificial Intelligence ZJUT Hangzhou310056 China Institute of Cyberspace Security College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

he rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalances involving account interaction frequencies and interaction types in the Ethereum transaction environment pose significant challenges to data mining-based fraud detection research. To address this, we first propose the concept of meta-interactions to refine interaction behaviors in Ethereum, and based on this, we present a dual self-supervision enhanced Ethereum fraud detection framework, named Meta-IFD. This framework initially introduces a generative self-supervision mechanism to augment the interaction features of accounts, followed by a contrastive self-supervision mechanism to differentiate various behavior patterns, and ultimately characterizes the behavioral representations of accounts and mines potential fraud risks through multi-view interaction feature learning. Extensive experiments on real Ethereum datasets demonstrate the effectiveness and superiority of our framework in detecting common Ethereum fraud behaviors such as Ponzi schemes and phishing scams. Additionally, the generative module can effectively alleviate the interaction distribution imbalance in Ethereum data, while the contrastive module significantly enhances the framework’s ability to distinguish different behavior patterns. The source code will be available in https://***/GISec-Team/Meta-IFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Data Association Method Based on Azimuth Inverse Cotangent Value

A Data Association Method Based on Azimuth Inverse Cotangent...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Nan Zou Muze Yin Guangpu Zhang Zhenqi Yang Pengbo Ma Dongyu Li National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China Science and Technology on Electronic Information Control Laboratory Chengdu China

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In this paper, we address the challenge of data association in scenarios involving multiple observers for underwater non-cooperative localization, where it is unclear how measurement data from different stations corresponds to the same target. We propose a novel data association method that leverages the inverse cotangent of azimuthal sequence features. The method first calculates the sequence of cotangent values measured by each station's bearing in a limited time. Linearly analyzes the sequence to obtain the slope, and finally realizes the data association according to the characteristic that the inverse cotangent value of the slope corresponds to the target's motion heading. The simulation results show that the proposed method has better association localization ability compared with the traditional method, overcomes the problem of the conventional method that the computational volume is too large when the number of observation stations and targets is large, and ensures the association correctness under a certain error.

关键词： Location awareness Power engineering Accuracy Azimuth Simulation Noise Observers Time measurement

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A Long Synthetic Aperture Passive Localization Method Using Two Planes

A Long Synthetic Aperture Passive Localization Method Using ...

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2021 CIE International Conference on Radar, Radar 2021

作者： Chen, Bowei Wang, Yuqi Sun, Guang-Cai Xing, Mengdao Zhang, Zijing Yang, Xiaoniu Xidian University National Laboratory of Radar Signal Processing Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665498142

A long synthetic aperture passive localization algorithm is proposed in this paper. First of all, the Doppler change rate of signal can be measured by the image contrast and then, grid points are divided within the maximum possible latitude and longitude of the emitter location, the Doppler change rate at each grid point can be calculated by using known position and velocity of the receiver. The grid points at which the calculated Doppler change rate is equal to the value measured by the received signal form an isoline. Two receivers can draw two isolines on the ground and the intersection of the isolines gives estimate of the emitter location. © 2021 IEEE.

关键词： Azimuth focusing Doppler change rate Passive localization Synthetic aperture localization

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An Optimized Method of Packet Loss Rate under Backup Path Protection in OBS Network 3

An Optimized Method of Packet Loss Rate under Backup Path Pr...

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Meng, Aodong Chen, Yucheng Dai, Jiahao Chen, Yongqun 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. © Published under licence by IOP Publishing Ltd.

关键词： Network topology

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Rethinking Graph Transformer Architecture Design for Node Classification

arXiv

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arXiv 2024年

作者： Zhou, Jiajun Chen, Xuanze Xie, Chenxuan Yu, Shanqing Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Binjiang Institute of Artificial Intelligence ZJUT Hangzhou310056 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing. However, this also imposes several limitations in node classification applications: 1) nodes are susceptible to global noise;2) self-attention computation cannot scale well to large graphs. In this work, we conduct extensive observational experiments to explore the adaptability of the GT architecture in node classification tasks and draw several conclusions: the current multi-head self-attention module in GT can be completely replaceable, while the feed-forward neural network module proves to be valuable. Based on this, we decouple the propagation (P) and transformation (T) of GNNs and explore a powerful GT architecture, named GNNFormer, which is based on the P/T combination message passing and adapted for node classification in both homophilous and heterophilous scenarios. Extensive experiments on 12 benchmark datasets demonstrate that our proposed GT architecture can effectively adapt to node classification tasks without being affected by global noise and computational efficiency limitations. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Genetic Algorithm for Block Relocation in Automated Warehouse 5

Genetic Algorithm for Block Relocation in Automated Warehous...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Gang Lian, Mingchang Ding, Wei Chen, Songhang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350001 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9781643684840

This paper is based on the research background of block relocation in the automated warehouse and proposes a solution utilizing AGV to relocate goods within the warehouse. Building upon the automated warehouse layout and block relocation process, we formulate a mathematical model to minimize the the number of AGV moving goods and the total travel distance of the AGV. To solve the model, we develop an enhanced genetic algorithm, which zones the block relocation process into multiple stages. Each stage integrates conventional heuristic algorithms to enhance the efficiency of the solution process. The improved genetic algorithm can effectively solve the problem, significantly enhancing the automation level of the warehouse. © 2024 The Authors.

关键词： Genetic algorithms

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