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5th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2023

作者： Wang, Haoyuan Tian, Yue Li, Wenda Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen China University College London Department of Security and Crime Science London United Kingdom

ISBN: (纸本)9798350333442

A clustering algorithm based Ultra Dense Deployment is proposed to address the problem of establishing a reasonable interference model and allocating ABS reasonably to minimize interference and maximize throughput in Ultra Dense Deployment. By analyzing the distribution pattern of low-power nodes in Ultra Dense Deployment, the interference generated by the cluster center is used to replace the interference of other nodes in the class. According to the principle of interference minimization, a distributed allocation scheme for ABS time slots based on K-means clustering is proposed, and a polling algorithm is used for user scheduling. Simulation results show that compared with the generalized interference model and traditional binary models, the interference calculation is more accurate, The throughput of the system is also higher. © 2023 IEEE.

关键词： K-means clustering

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A Mixed Intrusion Detection system utilizing K-means and Extreme Gradient Boosting 2

A Mixed Intrusion Detection System utilizing K-means and Ext...

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2022 2nd International Conference on Communication technology and information technology, ICCTIT 2022

作者： Lv, Haifeng Ji, Xiaoyu Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The intrusion detection system (IDS) plays an important part because it offers an efficient way to prevent and mitigate cyber attacks. Numerous deep learning methods for intrusion anomaly detection have been developed as a result of recent advances in artificial intelligence (AI) in order to strengthen internet security. The balance among the high detection rate (DR), the low false alarm rate (FAR) and disaster of dimensionality is the crucial apprehension while devising an effective IDS. For the binary classification of intrusion detection systems, we present in this study a mixed model called K-means-XGBoost consisting of K-means and (Extreme Gradient Boosting, XGBoost) algorithms. The distributed computation of our method is achieved in Spark platform to rapidly separate normal events and anomaly events. In phrases of accuracy, DR, F1-score, recall, precision, and error indices FAR, the proposed model's performance is measured via the well-known dataset of NSL-KDD. The experimental outcomes indicate that our method is outstandingly better among accuracy, DR, F1-score, training time, and processing speed, compared to other models which are recently created. In particular, the accuracy, F1-score, and DR of the proposed model can achieve as high as 93.28%, 94.39%, and 99.22% in the NSL-KDD dataset, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Cheng Chai, Songyao Zhu, Hangyu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350304855

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank. © 2023 IEEE.

关键词： Crime

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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A Blackbox Fuzzing Based on Automated State Machine Extraction

A Blackbox Fuzzing Based on Automated State Machine Extracti...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Zhaowei Zhang Wenjing Yu Zibin Wang Youlin Xiang National Key Laboratory of Science and Technology on Information System Security Beijing China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing.

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Time Slot Correlation-Based Caching Strategy for information-Centric Satellite Networks 1

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11th EAI International Conference on MOBILe Wireless MiddleWARE, Operating systems and Applications, MOBILWARE 2022

作者： Xu, Rui Di, Xiaoqiang Chen, Jing Cao, Jinhui Luo, Hao Wang, Haowei Qi, Hui He, Xiongwen Lei, Wenping School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China School of Information Engineering Henan Institute of Science and Technology Xinxiang453003 China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun130022 China Information Center Changchun University of Science and Technology 130022 Changchun China Beijing Institute of Spacecraft System Engineering Beijing100094 China Beijing Institute of Space Mechanic and Electricity Beijing100094 China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

With the successful manufacture of highly stable and high-performance satellite processors, caching on satellites has become possible. information Centric Networking (ICN) is introduced to satellite networks to address the growing need for diverse data services. However, existing caching policies in ICN still suffer from the shortage of rational planning of cache locations and redundancy of cache contents. Consequently, the time slot correlation-based caching strategy for satellite networks (TSCCS) is proposed. Firstly, the index of time slot correlation is designed to quantify the neighboring time slots’ coupling relationship, and then the DSAM network model with time slot correlation is established. Secondly, the significance of satellite nodes is evaluated from the local and global perspectives respectively based on the eigenvector centrality algorithm to filter out the dynamic set of cache nodes. Finally, probabilistic caching is performed at the set of caching nodes with the goal of reducing cached content redundancy, considering content popularity and access delay. The simulated outcomes indicate that the TSCCS caching policy is superior to the comparison method in the areas of cache hit rate, request latency, and server load. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Satellites

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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Design of power supply system for portable aviation inspection equipment 3

Design of power supply system for portable aviation inspecti...

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2023 3rd International Conference on Measurement Control and Instrumentation, MCAI 2023

作者： Yang, Yang Ai, Hao Li, Shibo Xiao, Guokai 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 Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Given that airborne electronic equipment may have a variety of wiring methods and may encounter a set of systems using multiple power supplies, it is necessary to take some measures on the circuit to ensure the normal work of the power supply and the post-circuit. A portable aviation detection equipment power supply system is designed in this paper. The system supports simultaneous access of two power supplies to identify and judge the input power supply and switch between the battery and the external power supply, effectively avoiding the security risks caused by battery backflow current. The battery power is monitored in real-time through the battery power detection circuit and will be reminded when the battery power is too low. Since the circuit uses a MOS tube to control the switching of the power supply, there is almost no voltage drop loss. In addition, the EMC protection circuit is set up at the power supply side to further improve the safety and reliability of the system. © Published under licence by IOP Publishing Ltd.

关键词： Oscillators (electronic)

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A-FAR: Research on Anti-obscuring Face Recognition Based on Improved FaceNet Model 11

A-FAR: Research on Anti-obscuring Face Recognition Based on ...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Zhu, Biaokai Zhang, Yangyang Li, Lu He, Yuhong Song, Yiting Li, Xiafei Yin, Ruiting Song, Jie Li, Ping Liu, Sanman Zhao, Jumin Taiyuan University of Technology Shanxi Police College Cyber Security Department Taiyuan China Shanxi Police College Cyber Security Department Taiyuan China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou China Anhui University School of Computer Science and Technology Hefei China Taiyuan University of Technology College of Information and Computer Jinzhong China

ISBN: (纸本)9798350345346

Face recognition and cloud computing are the current hot research areas, in real life, face image acquisition is often affected by various factors, such as occlusion, lighting and facial posture, many application scenarios require in the occlusion environment to be able to achieve face recognition. The objective of this paper is to realize a system for collecting and preprocessing face images, processing, analyzing and recognizing them on the cloud platform. An occluse-resistant face recognition algorithm based on the improved FaceNet model is proposed. The system uses MTCNN for face detection and the improved FaceNet for face recognition. After a series of experiments, the system performs well in the evaluated data set, with an accuracy rate of 94.76%. © 2023 IEEE.

关键词： Face recognition

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Cloud Based AIVR Training system for Emergency Rescue 12

Cloud Based AIVR Training System for Emergency Rescue

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Dong, Meiya Zhu, Biaokai Zhao, Jumin Song, Jie School of Computer Science Peking University Beijing China Suzhou Industrial Technology Institute Zhejiang University Suzhou China Cyber Security Shanxi Police College China College of Information and Optical Engineering Taiyuan University of Technology Taiyuan China Cyber security Intelligent Policing Key Laboratory of Sichuan Province Luzhou China

ISBN: (纸本)9798331511074

This paper mainly discusses the current situation and existing problems of training exercises in emergency medicial scenarios, as well as the importance and necessity of developing an AI training system for emergency medical scenarios. First, we introduce the current status of emergency medical training and emphasize the potential value of AI technology in this field. Then, the goal of developing an AI training system is defined as improving the efficiency and quality of medical education and training. We summarize the advantages of AI technology in emergency medical exercises and make a review and selection analysis of its related technologies. In the design and implementation stage, we describe in detail the requirement analysis, function design, system architecture, and core module development of the emergency medical AI training system. After system integration and testing, we verified the feasibility and effect of the system. In the process of evaluation and optimization, we evaluated the performance and user experience of the system and put forward optimization strategies and security considerations for the problems. In the application case analysis, we collect user feedback to verify its efficiency and carry out an in-depth analysis of the advantages and disadvantages of the system. Finally, in the conclusion and prospect part, we summarize the research results, point out the existing problems and improvement directions and look forward to the future development trends and challenges of AI training systems for emergency medical scenarios. © 2024 IEEE.

关键词： AI training AIVR system Emergency Rescue

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