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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Ruan, Zhiqiang Yang, Yuchen Chen, Lejia Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350312461

This paper investigates a new attack method called "near-source attack". It leverages the broadcast frames of the 802.11 protocol to establish a hidden tunnel and bypass physical isolation networks or air-gapped networks. We first analyze and implement a common technology known as Ghost Tunnel, which allows the attacker to control the target host and transmit information without being detected. However, this method suffers from frame loss, repeated frame, and attack transparency. We then propose an improved solution to deliver malicious programs to the target host using a modified BadUSB hardware device. Once the attackers successfully get in the isolated networks, they can bypass security protection devices and exploit vulnerabilities of communication protocols, so that they can remote control of target devices and hidden data transmission. We further conducted experiments to verify the feasibility and effectiveness of this attack scheme. The results indicate that the attack logic is capable of inducing the target host to engage in covert communication. Finally, we give some defense measures for such attacks. © 2023 IEEE.

关键词： Remote control

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Causal Label Enhancement

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IEEE Transactions on Knowledge and Data engineering 2024年第7期37卷 3933-3946页

作者： Liu, Xinyuan Zhu, Jihua Yue, Kun Zheng, Qinghai Li, Zhongyu Tian, Zhiqiang Zhu, Mingchen Xi'an Jiaotong University School of Software Engineering Xi'an China Yunnan University School of Information Science and Engineering Kunming China Fuzhou University College of Computer and Data Science Fuzhou China

Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM. © 1989-2012 IEEE.

关键词： Contrastive Learning

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A Novel Visual Transformer for Long-Distance Pipeline Pattern Recognition in Complex Environment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2933-2945页

作者： Zhu, Chengyuan Pu, Yanyun Yang, Kaixiang Yang, Qinmin Chen, C.L. Philip Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310007 China Huzhou Institute of Zhejiang University Zhejiang Huzhou313000 China South China University of Technology College of Computer Science and Engineering Guangdong Guangzhou510006 China

The effective warning of dangerous events along long-distance pipelines is critical to ensure the safety of oil and gas transportation. Distributed optical fiber sensing (DOFS) technology can assist operators to identify threat vibration signals. However, due to the complex and changeable environmental background noise along long-distance pipeline, most of the existing methods only extract one-dimensional features of the signal, making it difficult to distinguish various types of environmental noise, strong interference, and dangerous events. Besides, the samples of different classes in the actual scene are unbalanced. The sample size of dangerous events is often smaller than others. To address these problems, we use image encoding to transform the time series signals collected by the DOFS system into image data, and fully extract the time dependence and the correlation between different elements in the signal. Moreover, a visual transformer model PipelineADWinT is proposed. The self-attention mechanism of diagonal-axial window designed in this model can perfectly combine image encoding features, and obtain local to global multiscale features through hierarchical structure. By optimizing the loss function, the model's ability to handle the class imbalance problem is enhanced. The experimental results show that PipelineADWinT has more comprehensive classification performance and fewer false alarms than all the baseline models, which proves the effectiveness and superiority of the model. © 2020 IEEE.

关键词： Pattern recognition

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Unifying Large Language Models and Knowledge Graphs for Poultry Diseases Diagnosis 18th

Unifying Large Language Models and Knowledge Graphs for Po...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xu, Shengxiang Yao, Liang Huang, Chenyu Jiang, Guoxin Liu, Fan College of Computer and Software Hohai University Nanjing China Nanjing Research Institute of Electronic Engineering Nanjing China

ISBN: (纸本)9789819610709

The rapidly evolving biometric recognition technology has been widely applied in the detection and diagnosis of biological diseases. However, in the poultry domain, the scarcity of case information and the difficulty in data collection result in lower accuracy in disease diagnosis and treatment. Even though multi-modal large language models possess remarkable capabilities, they cannot be fully relied upon due to the hallucination problem. To address these issues, we propose PoultryAssistant, a novel approach that unifies LLMs with knowledge graphs to enhance diagnostic accuracy in poultry disease diagnosis. Specially, a poultry disease knowledge graph is constructed by utilizing entity-relation extraction algorithms. Then a strategy combining large language models and knowledge graphs is proposed to achieve diagnostic results through multi-source information interaction. Furthermore, extensive experiments on a poultry disease dataset demonstrate the effectiveness of our approach, marking a significant advancement in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Research on Intelligent Voice Broadcast System for Smart Water Conservancy 19

Research on Intelligent Voice Broadcast System for Smart Wat...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Huang, Zhenqian Chen, Lanxin College of Computer Science and Software Engineering Hohai University Nanjing China University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9798331507817

Smart water management imposes higher demands on the real-time capabilities and intelligence of watershed information monitoring systems. Therefore, we propose an intelligent voice broadcast system for monitoring water information, aimed at achieving efficient and accurate dissemination of warning information. This system can be deployed and integrated into existing water information monitoring systems to enable real-time voice broadcasting of water-related information. To ensure the system’s timeliness and effectiveness, we propose a Performance Optimization Mechanism for Data Processing, Priority control for voice broadcasting, a Content Optimization Mechanism, and a Timeliness control Mechanism. Comparative experiments demonstrate that the proposed intelligent voice broadcast system significantly enhance the efficiency and accuracy of water information dissemination, providing an innovative solution for real-time monitoring and early warning in water management. © 2025 IEEE.

关键词： Information management

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A Multi-robot Exploration Approach for Larger Maps with Unreliable Communications

A Multi-robot Exploration Approach for Larger Maps with Unre...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Lingxin Luo, Haibo College of Computer and Data Science College of Software FuZhou University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

The issues of limited communication distance and task capability are rarely discussed in designing multi-robot cooperative methods. However, these are important problems that cannot be avoided in practical applications. In this paper, simulation experiments are carried out on some task maps that are larger than the team's task ability. The effect of the dispersion of members on the team's task performance is tested, and the existing methods are improved by introducing long-range communication to auxiliary data sharing. At the same time, we use the calculation of Energy-Benefit to improve the task selection method based on the minimum distance to the target. Compared with existing strategies, the improved scheme performs better on communication-constrained large-map exploration tasks. © 2022 IEEE.

关键词： Multipurpose robots

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios 20

A Method Based on Genetic Programming to Automatically Const...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ma, Yan Zhang, Changsheng Gao, Yan Guo, Ying School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (纸本)9798350356328

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated. © 2024 IEEE.

关键词： Genetic programming

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： Data enhancement LSTM deep residual network VAE

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ma, Wenqiang Hau, Yaqing Yu, Hua Tong, Xiangrong Zhu, Zexuan Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350308365

Deep neural networks are powerful and popular learning models;however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem. © 2024 IEEE.

关键词： Optimization algorithms

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A completed region contrast binary pattern for texture image classification 3

A completed region contrast binary pattern for texture image...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Xu, Xiaochun Xiao, Tong College of Computer and Control Engineering Minjiang University Fuzhou China International Digital Economy College Minjiang University Fuzhou China

ISBN: (纸本)9781510680449

Texture is a key and basic visual cue for various image processing applications. To capture rich and discriminative local texture information, this paper develops a completed region contrast binary pattern (CRCBP), which introduces a novel pixel-region contrast measure scheme to extract contrast difference information between the pixel and its local background region. The proposed CRCBP consists of three components: local difference sign, local difference magnitude, and local pixel-region contrast measure sign. According to the local neighborhood encoding mechanism, we can get three sub-patterns: sign binary pattern, magnitude binary pattern, and local region contrast binary pattern. To achieve completed texture feature representation, these three sub-patterns are jointly combined to obtain the completed region contrast binary pattern. The comparative evaluations on benchmark database shows that the proposed CRCBP descriptor yields the state-of-the-art classification performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Pixels

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