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14th International Conference on computer engineering and networks, CENet 2024

作者： Mo, Zixi Zhuge, Chengchen Ma, Zhuo Li, Xiang Zhang, Heyu Zhang, Xunrui Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Nanjing China Department of Public Security of Jiangsu Province Key Laboratory of Digital Forensics Nanjing China

ISBN: (纸本)9789819642441

To address the problems of low password security and the use of the same password on multiple network platforms, we analyze the major techniques and shortcomings of the existing personal password manager, and a personal password management system based on domestic cryptographic algorithm is designed. This system mainly consists of four parts: identity authentication module, password management module, encryption and decryption module of domestic cryptographic algorithm, and visualization interaction module, which are responsible for user identity authentication, addition, deletion, modification and checking of passwords, encrypting and protecting passwords using SM2 in conjunction with SM4 algorithm, and displaying data on the front-end page through the interface and beautifying it, respectively. Compared with the traditional system, this system is easy to use, more secure in password preservation, more practical, and creatively uses the "SM2 + SM4" domestic cryptographic algorithms encryption combination instead of the traditional "RSA + AES" encryption combination, which is more reliable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human resource management

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Design and Implementation of a BERT Based Weibo Public Opinion Monitoring System 14th

Design and Implementation of a BERT Based Weibo Public Opini...

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14th International Conference on computer engineering and networks, CENet 2024

作者： Chen, Shun-Chao Zhu, Tao Pang, Zi-An Xu, Chen Department of Computer Information and Cyber Security Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Jiangsu Police Institute Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province NAN Jing210031 China

ISBN: (纸本)9789819642281

With the rapid development of online We Media, it is not easy to monitor and guide massive public opinion information. The massive forwarding, emotional polarization, and mixed rumors of blog posts are highly likely to cause online public opinion events. A Weibo public opinion monitoring system based on bidirectional encoder representation from transformers (BERT) is proposed to support the full lifecycle guidance for public opinion. After Weibo text data is crawled from blog posts of important informants according to the lifecycle stages, and preprocessed for data cleaning and text vectorization, an emotional tendency classification and rumor detection model with the way of ensemble learning is proposed with the latent Dirichlet allocation (LDA) model and three BERT-based combining model. For extracting emotional tendency information efficiently, the LDA model is applied to extract topics on emotional polarity in blog posts. In order to address the issue of long text data caused by the lifting of restrictions on short posts on Weibo, three BERT-based model are ensembled together to achieve better performance. The experiment test demonstrates that the evolution of online public opinion can be described efficiently by this system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BERT LDA public opinion monitoring We Media

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APK Automated Analysis System Based on Machine Learning 14th

APK Automated Analysis System Based on Machine Learning

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14th International Conference on computer engineering and networks, CENet 2024

作者： Tang, keyan Liang, Guangjun Chen, Mengxuan Lu, You Zhang, Yang Xu, Zhongping Xu, Yiquan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Nanjing China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu Province Nanjing China

ISBN: (纸本)9789819642441

This article first analyzes and compares the advantages and disadvantages of various traditional data mining methods and machine learning algorithms. Then, a new model for identifying Android malicious code was proposed: an Android malicious code detection model based on Java code analysis and deep learning algorithms. The model first extracts Java source code from Android software packages. Furthermore, based on the object-oriented features of the Java language, an inheritance and cross reference relationship graph between various classes is constructed, and the graph is mapped to a grayscale image through its adjacency matrix. Finally, a CNN model is used for classification training to identify malicious programs. By analyzing and comparing the advantages and disadvantages of various feature extraction and selection methods. Using data cleaning and principal component analysis to remove features unrelated to classification, thereby reducing data complexity and improving model prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Java programming language

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Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

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Journal of Earth Science 2024年第3期35卷 1035-1043页

作者： Hang He Chao Ma Shan Ye Wenqiang Tang Yuxuan Zhou Zhen Yu Jiaxin Yi Li Hou Mingcai Hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyCollege of Computer Science and Cyber SecurityChengdu University of TechnologyChengdu 610059China Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources Chengdu University of TechnologyChengdu 610059China School of Information Engineering China University of Geosciences(Beijing)Beijing 100083China

Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual *** the rapid development of science and technology,a large number of textual reports have accumulated in the field of ***,many non-hot topics and non-English speaking regions are neglected in mainstream geoscience databases for geological information mining,making it more challenging for some researchers to extract necessary information from these *** Language Processing(NLP)has obvious advantages in processing large amounts of textual *** objective of this paper is to identify geological named entities from Chinese geological texts using NLP *** propose the RoBERTa-Prompt-Tuning-NER method,which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named entities in low-resource dataset *** RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word ***,we conducted experiments on the constructed Geological Named Entity Recognition(GNER)*** experimental results show that the proposed model achieves the highest F1 score of 80.64%among the four baseline algorithms,demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

关键词： Prompt Learning Named Entity Recognition(NER) low resource geological text text information mining big data geology.

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Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks 39

Adversarial-Inspired Backdoor Defense via Bridging Backdoor ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yin, Jia-Li Wang, Weijian Lyhwa Lin, Wei Liu, Ximeng Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Lion Rock Labs of Cyberspace Security CTIHE Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adversarial attacks and observe two intriguing phenomena when applying adversarial attacks on an infected model implanted with backdoors: 1) the sample is harder to be turned into an AE when the trigger is presented;2) the AEs generated from backdoor samples are highly likely to be predicted as its true labels. Inspired by these observations, we proposed a novel backdoor defense method, dubbed Adversarial-Inspired Backdoor Defense (AIBD), to isolate the backdoor samples by leveraging a progressive top-q scheme and break the correlation between backdoor samples and their target labels using adversarial labels. Through extensive experiments on various datasets against six state-of-the-art backdoor attacks, the AIBD-trained models on poisoned data demonstrate superior performance over the existing defense methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Research on Satellite network Traffic Prediction Algorithm Based on Gray Wolf Algorithm Optimizing GRU and Spatiotemporal Analysis 15

Research on Satellite Network Traffic Prediction Algorithm B...

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15th International Conference on Communication Software and networks, ICCSN 2023

作者： Cong, Ligang Shi, Baoyu Di, Xiaoqiang Ding, Huiying Chen, Chuanhui School of Computer Science and Technology Changchun University of Science and Technology Changchun China Changchun University of Science and Technology Jilin Key Laboratory of Network and Information Security Changchun China

ISBN: (纸本)9798350329360

Accurate traffic prediction is crucial for the performance improvement of satellite networks, which can help balance network load, optimize routing, and effectively improve network performance. To better plan traffic while capturing the spatial and temporal dependencies of traffic, we propose a new neural network-based traffic prediction method for satellite networks, namely the temporal graph convolutional attention (ACW-GRU) model, which is combined with the Graph Convolutional Neural network (GCN), the Gated Recurrent Unit (GRU), and the Soft Attention mechanism. The GCN is used to analyze the complex topology to capture spatial correlation, and the GRU is used to analyze the dynamic changes of traffic data to capture temporal correlation, while the Grey Wolf Optimization (GWO) is used to optimize the hyperparameters of the GRU model, and the soft attention mechanism is used to integrate the global temporal information and adjust the importance at different time points. The importance of the traffic data at distant time points is also not ignored, thus improving the accuracy of the prediction. The simulation results show that the ACW-GRU model is superior in fitting the flow data compared with the traditional GRU, LSTM, and FARIMA models, and its root mean square error (RMSE) is reduced by 24.7%, 40.4%, and 51.2%, respectively. Therefore, the ACW-GRU model can predict the traffic data of the satellite network more accurately, thus providing clearer guidance for defining the demand of applications and the allocation of network resources. © 2023 IEEE.

关键词： Topology

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SyntaxBridge: Protocol Description Transformer for Enhanced Formal Analysis of security Protocols 23

SyntaxBridge: Protocol Description Transformer for Enhanced ...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Cai, Liujia Yu, Tong Li, Yumeng Lu, Siqi Dong, Hanjie Cai, Guangying Li, Guangong Wang, Yongjuan Information Engineering University Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Hainan University School of Information and Communication Engineering Haikou China Zhengzhou University School of Computer Science and Artificial Intelligence Zhengzhou China

Currently, formal analysis of security protocols under the symbolic model has become a crucial approach in the field of protocol analysis, owing to its intuitiveness and universality. However, researchers using various symbolic security protocol verifiers need to thoroughly understand the strengths and weaknesses of these verifiers, as well as their specific encoding models. A&B protocol description and CAS+ protocol description are highly favored by researchers due to their intuitiveness, but their limitation lies in the fact that these two descriptions can only be used for their corresponding verifiers. In this study, we constructed a bridge connecting different symbolic security protocol verifiers. Building upon existing compilers A&B to TAMARIN and CAS+ to AVISPA, we extended A&B to CAS+ and CAS+ to A&B using the lexical analyzer Flex and the parser generator tool Bison. Thus, this bridge enables leveraging the respective strengths of each symbolic security protocol verifier in a short period. It also greatly facilitates security protocol researchers in analyzing protocols using intuitive protocol descriptions through various formal analysis tools. © 2024 IEEE.

关键词： HTTP

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Feature Aggregation network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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Characterization and Properties of Bent-Negabent Functions

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Chinese Journal of Electronics 2022年第4期31卷 786-792页

作者： JIANG Niu ZHAO Min YANG Zhiyao ZHUO Zepeng CHEN Guolong School of Mathematical Sciences Huaibei Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University School of Cyber Science University of Science and Technology of China School of Computer Engineering Bengbu University

A further characterization of the bentnegabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.

关键词： Boolean function Bent-negabent function Walsh-Hadamard transform Nega-Hadamard transform

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TFSG: A Self-Supervised Guided Graph Learning Method for Traffic Flow Prediction

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IEEE Internet of Things Journal 2025年第13期12卷 24345-24357页

作者： Zheng, Yingran Luo, Chao Shao, Rui Shandong Normal University School of Information Science and Engineering Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China China Mobile Shandong Co. Ltd center of network optimization Jinan250014 China

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules etc., distance bias is non-trivial to affect traffic flow in reality. Besides, the distance based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multi-head self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified. © 2014 IEEE.

关键词： Self-supervised learning

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