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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Huang, Zhendong Fan, Xuyan Li, Zhiqi Zhao, Changzhi Chen, Guangxuan Liu, Yuling People's Public Security University of China Department of Information and Network Security Beijing China Ruian Public Security Bureau Wenzhou China Zhejiang Police College Department of Computer and Information Security Hangzhou China The National Information Center of China Customs Beijing China Beijing State Grid Siji Network Security Technology Limited Company Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350333664

Web API networks comprise a multitude of API services, and compared to traditional network environments, they possess unique security requirements and face distinct threats. This article delves into the security characteristics of Web API networks and the particular threats they encounter, establishing evaluation criteria for anomaly detection models tailored to Web API networks and their associated scenarios. Through an integrated assessment of numerous studies, we meticulously compare and analyze various models in terms of false positive rates, robustness, detection latency, and their adaptability and detection capabilities. The findings reveal that while many models excel in detecting singular attacks within the Web API network context, their scope of attack detection is often constrained, making it challenging to comprehensively address the myriad threats Web API networks confront. Moreover, several unresolved challenges persist in the research, such as the scarcity of real-world datasets, inadequate model generalization capabilities, and subpar robustness. © 2023 IEEE.

关键词： anomaly detection API calls model evaluation scenario application Web API network

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Quantum Privacy-Preserving Price E-Negotiation

arXiv

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arXiv 2023年

作者： Liu, Wen-Jie Li, Chun-Tang Zheng, Yu Xu, Yong Xu, Yin-Song Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China

Privacy-preserving price e-negotiation (3PEN) is an important topic of secure multi-party computation (SMC) in the electronic commerce field, and the key point of its security is to guarantee the privacy of seller’s and buyer’s prices. In this study, a novel and efficient quantum solution to the 3PEN problem is proposed, where the oracle operation and the qubit comparator are utilized to obtain the comparative results of buyer’s and seller’s prices, and then quantum counting is executed to summarize the total number of products which meets the trading conditions. Analysis shows that our solution not only guarantees the correctness and the privacy of 3PEN, but also has lower communication complexity than those classical ones. © 2023, CC BY.

关键词： Comparators (optical)

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Small Target Detection from DIKW Model Perspective

Small Target Detection from DIKW Model Perspective

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Wang, Yujuan Peng, Ling Yu, Riji Juan, Hu Yan, Shankai Zhang, Bin Guangzhou Xinhua University Machong Town Guangdong Province Dongguan City China Huanggang Real Estate Registration Center Huanggang Land and Resources Bureau Huanggang City China School of Art Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China The Discipline Inspection and Supervision College of Huanggang Normal University Huanggang City China School of Computer Science Technology Hainan University Haikou China Network Information Center Xinjiang University of Political Science and Law Tumushuke China Department of Computer Science City University of Hong Kong Hong Kong School of Remote Sensing and Information Engineering Wuhan University Hong Kong Hong Kong

ISBN: (纸本)9781510680012

Through the revelation and internal logic relationship of the DIKW model, the logic of data, information, knowledge, and wisdom in small target detection research initially built a new point of view, and did some basic tasks for small target detection and research, so as to use it to use it to do some basic tasks for small target detection and research. Small target detection research model under DIKW. The DIKW Information System was introduced into the field of small target testing. First, the DIKW model was analyzed. The framework of data information -information knowledge -intelligent framework was studied, and small target detection methods of one -stage and two -stage stages were studied, such as CNN -based methods, transformer methods and mixed methods. This article analyzes the basic network framework of small target detection, and builds data, information, knowledge and wisdom in small targets detection research. © 2024 SPIE.

关键词： computer vision

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Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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DQ-WiAPDoM: A DQN-based AP Deployment Optimization Method for Wi-Fi FTM Positioning

DQ-WiAPDoM: A DQN-based AP Deployment Optimization Method fo...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Zihao Liu Han Li Bo Gao Ke Xiong Pingyi Fan School of Computer and Information Technology Beijing Jiaotong University Beijing China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

This paper focuses on how to optimize the deployment location of the anchor Wi-Fi access points (APs) in order to enhance the indoor positioning performance of the finetime measurement (FTM) protocol. Compared to conventional Wi-Fi positioning, FTM protocol is able to achieve relatively good indoor positioning performance since it estimates distance between a sender and its receiver with the flight duration of Wi-Fi signals. However, the FTM-based positioning accuracy in NLoS environments is still not high, which restricts its application in indoor scenarios. To this end, we formulate an optimization problem to maximize the total number of LoS paths by optimizing the locations of the anchor APs. To solve the problem, we propose a Deep Q-network (DQN)-based AP deployment optimization method (DQ-WiAPDoM), which utilizes DQN to optimize the locations of APs such that the total number of LoS paths over all covered area is maximized. Experimental results show that DQ-WiAPDoM converges well and is able to reduce the average positioning error efficiently.

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Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification

Good is Bad: Causality Inspired Cloth-debiasing for Cloth-ch...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhengwei Yang Meng Lin Xian Zhong Yu Wu Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science and Artificial Intelligence Wuhan University of Technology

Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re- IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM 1 1 Codes will publicly available at https://***/BoomShakaY/AIM-CCReID., is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods.

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Navigating the Risks: A Review of Safety Issues in Large Language Models

Navigating the Risks: A Review of Safety Issues in Large Lan...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Haiyang Wang Yihao Li Yue Wang Pan Liu Pengxiao Li Institute of Network Technology (Yantai) Yantai China School of Information and Electrical Engineering Ludong University Yantai China Faculty of Business Information Shanghai Business School Shanghai China National Computer Network Emergency Response Technical Team/Coordination Center Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Since the advent of Large Language Models (LLMs) such as ChatGPT, natural language processing has seen significant progress, providing timely, context-aware, and intelligent solutions across various domains. While users benefit greatly from these advancements, increasing safety concerns have surfaced, highlighting vulnerabilities and their impact on daily tasks. It is crucial to understand the implications of LLMs and not simply trust their outputs blindly. This paper reviews literature on LLM safety issues including data privacy, security, adversarial attacks, and compliance with laws and ethical guidelines.. Specifically, limitations, risks, testing methods, and evaluation criteria from a safety perspective are discussed. Based on these insights, it proposes suggestions to mitigate current safety challenges. The goal is to aid researchers and developers in designing safer, more reliable LLMs and to enhance user interactions with these technologies.

关键词： Reviews Navigation Large language models Software quality Reliability engineering Safety Software reliability Security Surface treatment Testing

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Multi-Objective Discrete Extremal Optimization of Variable-Length Blocks-Based CNN by Joint NAS and HPO for Intrusion Detection in IIoT

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Lu, Kang-Di Huang, Jia-Cheng Zeng, Guo-Qiang Chen, Min-Rong Geng, Guang-Gang Weng, Jian Donghua University College of Information Science and Technology Shanghai201620 China Jinan University College of Cyber Security The National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou510632 China South China Normal University School of Computer Science Guangzhou510631 China

Industrial Internet of Things (IIoT) is an important part of industrial infrastructure but facing serious and evolving security threats in recent years. Deep learning has been widely considered as a promising solution for enhancing the security of IIoT. However, these existing deep learning models utilized in the intrusion detection of IIoT are manually developed that not only greatly rely on the experience of the designers but also is lack of utility due to the high model complexity. By taking into account the trade-off between the model performance and model complexity, this paper makes the first attempt to propose a multi-objective joint optimization method of neural architecture search (NAS) and hyper-parameter optimization (HPO) based on multi-objective discrete extremal optimization (MODEO) to automatically design a lightweight convolutional neural network (CNN) for the intrusion detection task of IIoT, abbreviated as MODEO-CNN. A novel hybrid variable-length encoding strategy is developed by combing binary and integer encoding to characterize both the neural architectures including the number of blocks, the blocks-based network topology and the corresponding architecture parameters in CNN block, and some important hyper-parameters including batch size, learning rate, weight optimizer and regularization. The individual-based discrete multi-objective evolutionary process of MODEO is designed to obtain the Pareto-optimal CNN models. Three widely-used IIoT intrusion detection datasets, including the Gas Pipeline, BoT-IoT, and Power System Attack datasets, have been used to illustrate the superiority of the proposed MODEO-CNN over the state-of-the-art hand-craft models and two single-objective fixed-length blocks-based NAS models in terms of accuracy, precision, recall, F1-Score, and model's million floating point operations. © 2004-2012 IEEE.

关键词： Encoding (symbols)

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Joint User Cooperation and Scheduling for Federated Learning in CFmMIMO networks

Joint User Cooperation and Scheduling for Federated Learning...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Han Bao Bo Gao Ke Xiong Pingyi Fan School of Computer and Information Technology Beijing Jiaotong University Beijing China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

This paper proposes a joint user cooperation and scheduling (JUCS) method to minimize the maximum transmission time among users in the synchronous federated learning (FL) architecture, since the user with the worst quality link limits the training performance of FL. To this end, an optimization problem is formulated to find the optimal user cooperation pairing and the power control coefficients. Meanwhile, as the links among users operate on the same frequency bands, the interface between them is taken into account in this paper. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fair scheduling (FS)-based algorithm to schedule the helping users to pair with the ones who are required to be assisted. For the second sub-problem, an algorithm based on deterministic policy gradient (DDPG) is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCS method shortens about 34.48% transmission time of the worst user in FL compared to the traditional one without JUCS.

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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