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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Bozheng Liao Guangxuan Chen Jiajian Wu Yingying Xu Qiang Liu Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (纸本)9781665479691

In the process of application server investigation and evidence collection of new types of network crime, this paper summarizes the analysis of common server clusters, virtual private network servers and IP-based voice transmission servers in public security investigation and forensic identification. based on the case analysis and the actual process of case investigation, a new type of network crime application server inspection method is proposed, which can be used to analyze new types of application servers in the process of law enforcement inspection and digital forensics practice.

关键词： Strips Digital forensics Inspection Search engines Virtual private networks Safety Servers

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Intelligence Analysis of Crime of Helping Cybercrime Delinquency based on Knowledge Ggraph

Intelligence Analysis of Crime of Helping Cybercrime Delinqu...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Qinhui Qi Guangxuan Chen Fangyi Ye Zihao Lin Qiang Liu Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (纸本)9781665479691

This paper studies the crime of helping cybercrime delinquency (CHCD) from two aspects: theoretical literature research and case empirical research. First of all, clarify the basic concept, development trend and case characteristics of the crime of facilitation, and make a comprehensive exposition of the crime of facilitation. Secondly, the basic situation analysis, co-word analysis, and keyword clustering of the theoretical research literature on the CHCD in the literature database in recent years are carried out, so as to obtain the research hotspots and evolution process of the CHCD, and analyze the research in the form of knowledge graph. Then, through the analysis and research of the crime cases, the relevant data and information are extracted from the judgment documents, and the regional differences of the crime, the characteristics of the criminal subject, the characteristics of the criminal behavior, and the judicial sentencing are comprehensively sorted out to provide information for the follow-up intelligence research and judgment support.

关键词： Automation databases Market research Information management Behavioral sciences data mining Computer crime

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A Spam Classification Method based on Naive Bayes 6

A Spam Classification Method Based on Naive Bayes

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6th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Ye, Fangyi Chen, Guanxuan Liu, Qiang Zhang, Lei Qi, Qinhui Hu, Bo Fan, Xiaoshi Institute for Cyberspace Intelligence and Crime Governance Zhejiang Police College Hangzhou China Key Laboratory of Public Security Informatization Application Based on Big-Data Architecture MPS Zhejiang Police College Hangzhou China Joint Logistics College of National Defence University Beijing China Cadet Brigade 4 Joint Operations College of National Defence University Shijiazhuang China

ISBN: (纸本)9781665431859

Currently, spam will do everything possible to bypass or destroy the spam classifier. This paper proposes a naive Bayesian spam classification method, and designs Bernoulli naive Bayes algorithm and polynomial naive Bayes algorithm to classify English spam and Chinese spam respectively. Experiments show that the method proposed in this paper has a high precision rate and recall rate, and can classify spam simply, accurately and efficiently. © 2022 IEEE.

关键词： Classifiers

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A novel high-fidelity reversible data hiding scheme based on multi-classification pixel value ordering

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Journal of Visual Communication and Image Representation 2025年 110卷

作者： Cui, Chen Li, Li Lu, Jianfeng Zhang, Shanqing Chang, Chin-Chen College of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Zhejiang Police College Hangzhou310018 China Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Pixel value ordering (PVO) is a highly effective technique that employs a pixel block partitioning and sorting for reversible data hiding (RDH). However, its embedding performance is significantly impacted by block size. To address this, an improved pixel-based PVO (IPPVO) was developed adopting a per-pixel approach and adaptive context size. Nevertheless, IPPVO only considers pixels below and to the right for prediction, neglecting other closer neighboring regions, leading to inaccurate predictions. This study presents a RDH strategy using multi-classification embedding to enhance performance. First, pixels are categorized into four classes based on parity coordinates, obtaining higher correlation prediction values using an adaptive nearest neighbor content size. Second, a new complexity calculation method is introduced, the complexity frequency of pixel regions to better differentiate between complex and flat regions. Finally, an effective embedding ratio and index value constraint are introduced to mitigate the challenge of excessive distortion when embedding large capacities. Experimental results indicate that the proposed scheme offers superior embedding capacity with low distortion compared to state-of-the-art PVO-based RDH methods. © 2025 Elsevier Inc.

关键词： Adaptive modification High-fidelity Multi-classification RDH

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High-speed and long-reach PON employing PM-DQPSK signal and coherent receiver

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Journal of Optical Communications 2023年第s1期44卷 S97-S105页

作者： Qin, Panke Meng, Hui Zhang, Lei Chen, Guangxuan College of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Joint Logistics College National Defence University Beijing100858 China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public Security Zhejiang Police College Zhejiang Hangzhou310053 China

The development of communication technology requires a new type of passive optical network (PON) architecture. The architecture needs to be based on time division multiplexing (TDM) of 40 Gbit/s downstream and 10 Gbit/s upstream, and it can realize TDM PON prototype. The PON system achieved the system distance of 120 km supporting 128 ONU with power budget of 53.5 dB via polarization multiplexing differential quadrature phase shift keying format signal which employs the coherent receiver and the digital signal processing. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Passive optical networks

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Long Text Classification based on BERT 5

Long Text Classification Based on BERT

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5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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A Compressed Deep Convolutional Neural Networks for Face Recognition 5

A Compressed Deep Convolutional Neural Networks for Face Rec...

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5th IEEE International Conference on Cloud Computing and big data Analytics, ICCCBDA 2020

作者： Yao, Li Key Lab. of Pub. Secty. Info. Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (纸本)9781728160245

Recent years, deep convolutional neural network has led to significant improvements in face recognition and becomes one of the most popular techniques in computer vision community. However, deep CNN model requires vast amounts of data and time for training and deploying. To solve this problem, we present a deep compact convolutional neural network for face representation. First we apply PCA for initializing convolution filter. Then we adopt DCT and binary hashing for extracting face features. The models are trained on the CASIA-webFace datasets under Caffe framework. Experimental results show that the proposed methods achieve competitive accuracy on the LFW verification benchmark. © 2020 IEEE.

关键词： Face recognition

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A Compressed Deep Convolutional Neural Networks for Face Recognition

A Compressed Deep Convolutional Neural Networks for Face Rec...

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IEEE International Conference on Cloud Computing and big data Analysis (ICCCBDA)

作者： Li Yao Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Zhejiang Police College Hangzhou China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

关键词： Feature extraction Convolution Face recognition Convolutional neural networks Face Principal component analysis Biological neural networks

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Research on Digital Forensics Framework for Malicious Behavior in Cloud 4

Research on Digital Forensics Framework for Malicious Behavi...

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4th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019

作者： Chen, Guangxuan Wu, Di Chen, Guangxiao Qin, Panke Zhang, Lei Liu, Qiang Zhejiang Police College Key Laboratory of Public Security Information Application Based on Big Data Architecture MPS Hangzhou China People's Procuratorate of Hangzhou Hangzhou China Wenzhou Public Security Bureau Wenzhou China Universidad Carlos III de Madrid Madrid Spain Henan Polytechnic University Jiaozuo China National Defence University Joint Services Academy Beijing China

ISBN: (纸本)9781728119076

The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods. © 2019 IEEE.

关键词： Cybersecurity

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Research on Digital Forensics Framework for Malicious Behavior in Cloud

Research on Digital Forensics Framework for Malicious Behavi...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Guangxuan Chen Di Wu Guangxiao Chen Panke Qin Lei Zhang Qiang Liu Key Laboratory of Public Security Information Application Based on Big Data Architecture MPS Zhejiang Police College Hangzhou China The People’s Procuratorate of Hangzhou Hangzhou China Wenzhou Public Security Bureau Wenzhou China Henan Polytechnic University Jiaozuo China Joint Services Academy National Defence University Beijing China

关键词： Cloud computing Digital forensics Computer crime Emergency services Virtual machining

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