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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Decentralized Intermittent Feedback Adaptive Control of Non-triangular Nonlinear Time-varying systems

arXiv

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arXiv 2022年

作者： Sun, Libei Huang, Xiucai Song, Yongduan The State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned Systems School of Automation Chongqing University Chongqing400044 China

This paper investigates the decentralized stabilization problem for a class of interconnected systems in the presence of non-triangular structural uncertainties and time-varying parameters, where each subsystem exchanges information only with its neighbors and only intermittent (rather than continuous) states and input are to be utilized. Thus far to our best knowledge, no solution exists priori to this work, despite its high prevalence in practice. Two globally decentralized adaptive control schemes are presented based on the backstepping technique, the first one is developed in a continuous fashion by combining the philosophy of the modified congelation of variables based approach with the special treatment of non-triangular structural uncertainties, which avoids the derivative of time-varying parameters and eliminates the limitation of the triangular condition, thus largely broadens the scope of application. By making use of the important property that the partial derivatives of the constructed virtual controllers in each subsystem are all constant, the second scheme is developed through directly replacing the states in the preceding scheme with the triggered ones. Consequently, the non-differentiability of the virtual control stemming from intermittent state feedback is completely obviated. The internal signals under both schemes are rigorously shown to be globally uniformly bounded with the aid of several novel lemmas, while the stabilization performance can be enhanced by appropriately adjusting design parameters. Moreover, the inter-event intervals are ensured to be lower-bounded by a positive constant. Finally, numerical simulation verifies the benefits and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Backstepping

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Dlr: Adversarial Examples Detection and Label Recovery for Deep Neural Networks

SSRN

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SSRN 2024年

作者： Han, Keji Li, Yun Ge, Yao Wang, Ruchuan Nanjing University of Posts and Telecommunications Wenyuan Road 9 Nanjing210046 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Wenyuan Road 9 Nanjing210046 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Wenyuan Road 9 Nanjing210046 China

Deep neural networks (DNNs) are demonstrated to be vulnerable to the adversarial example crafted by the adversary to fool the target model. Adversarial training and adversarial example detection are two popular methods to mitigate the adversarial issue. The adversarial training makes the target model recognize the adversarial example correctly for the image classification task, while it does not generalize well. The adversarial detection generalizes well but cannot help the target model recognize the adversarial example. We first define the label recovery task to address the adversarial issue for DNNs. Then we propose a new generative classifier and apply it to the adversarial example label recovery task. The proposed method is named as Detection and Label Recovery (DLR) defense method, which consists of Detector and Recover. Detector feeds the legitimate and adversarial examples to the target model and Recover, respectively. The Recover explores the ground-truth label of the detected adversarial example. DLR combines the merits of adversarial training and adversarial example detection. Experimental results demonstrate that our method outperforms some state-of-the-art adversarial training methods. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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The security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning

arXiv

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arXiv 2024年

作者： Pei, Wenbin Dai, Ruohao Xue, Bing Zhang, Mengjie Zhang, Qiang Cheung, Yiu-Ming Xia, Shuyin The School of Computer Science and Technology Dalian University of Technology Dalian116024 China Ministry of Education Dalian116024 China The School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand The Department of Computer Science Hong Kong Baptist University Hong Kong The Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class is of importance in many real-life applications. Hybrid sampling methods address this by oversampling the minority class to increase the number of its instances, followed by undersampling to remove low-quality instances. However, most existing sampling methods face difficulties in generating diverse high-quality instances and often fail to remove noise or low-quality instances on a larger scale effectively. This paper therefore proposes an evolutionary multi-granularity hybrid sampling method, called EvoSampling. During the oversampling process, genetic programming (GP) is used with multi-task learning to effectively and efficiently generate diverse high-quality instances. During the undersampling process, we develop a granular ball-based undersampling method that removes noise in a multi-granular fashion, thereby enhancing data quality. Experiments on 20 imbalanced datasets demonstrate that EvoSampling effectively enhances the performance of various classification algorithms by providing better datasets than existing sampling methods. Besides, ablation studies further indicate that allowing knowledge transfer accelerates the GP's evolutionary learning process. Copyright © 2024, The Authors. All rights reserved.

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Towards Distractibility Induced trust Management Using BlockChain for Edge Computing

Towards Distractibility Induced trust Management Using Block...

引用

International Conference on Image, Vision and intelligent systems, ICIVIS 2021

作者： Yang, Haochen Wang, Guanghui Dong, Lifeng He, Xin School of Software Henan University Kaifeng China Henan Provincial Engineering Research Center of Intelligent Data Processing Kaifeng China Henan International Joint Laboratory of Intelligent Network Theory and Key Technology Henan University Kaifeng China Institute of Intelligent Network System Henan University Kaifeng China Henan Jiuyu Tenglong Information Engineering Co. Ltd. Zhengzhou China

ISBN: (纸本)9789811669620

Edge computing solves massive data processing problems by utilizing the storage and computing functions of edge devices. Due to the openness of edge computing, distracted devices inevitably participate in edge computing and affect the network computing performance, which degrades trust management of edge devices. To solve the above issue, this paper proposes a trust management framework based on blockchain for edge computing scenarios (ECTMF). First, the ECTMF framework slices users and provides corresponding services based on different requirements. It evaluates the trustworthiness of end devices and distinguishes distracted devices in edge computing. Then, the Proof of Monitor (PoM) consensus mechanism is proposed to continuously monitor distracted end devices in edge computing. Simulations validate the effectiveness and efficiency of the ECTMF and PoM. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration

Effective Concurrency Testing for Go via Directional Primiti...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Zongze Jiang Ming Wen Yixin Yang Chao Peng Ping Yang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System ByteDance Beijing China School of Computer Science and Technology Huazhong University of Science and Technology China

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.

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Reinforcement Learning Methods for Fixed-Wing Aircraft Control

Reinforcement Learning Methods for Fixed-Wing Aircraft Contr...

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International Conference on systems and Informatics (ICSAI)

作者： Lun Li Xuebo Zhang Yang Wang Chenxu Qian Minghui Zhao College of Artificial Intelligence Nankai University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics Tianjin China Tianjin Municipal Data Management Center Tianjin China

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future.

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Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy

arXiv

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arXiv 2025年

作者： You, Zhichao Dong, Xuewen Li, Shujun Liu, Ximeng Ma, Siqi Shen, Yulong School of Computer Science & Technology Xidian University China Shaanxi Key Laboratory of Network and System Security Xi’an China University of Kent Canterbury United Kingdom College of Computer and Data Science Fuzhou University Fuzhou China School of System and Computing University of New South Wales CanberraACT2612 Australia

Reconstruction attacks against federated learning (FL) aim to reconstruct users’ samples through users’ uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are ineffective in FL with LDP since clipped and perturbed gradients obliterate most sample information for reconstruction. Besides, existing attacks embed additional sample information into gradients to improve the attack effect and cause gradient expansion, leading to a more severe gradient clipping in FL with LDP. In this paper, we propose a sample reconstruction attack against LDP-based FL with any target models to reconstruct victims’ sensitive samples to illustrate that FL with LDP is not flawless. Considering gradient expansion in reconstruction attacks and noise in LDP, the core of the proposed attack is gradient compression and reconstructed sample denoising. For gradient compression, an inference structure based on sample characteristics is presented to reduce redundant gradients against LDP. For reconstructed sample denoising, we artificially introduce zero gradients to observe noise distribution and scale confidence interval to filter the noise. Theoretical proof guarantees the effectiveness of the proposed attack. Evaluations show that the proposed attack is the only attack that reconstructs victims’ training samples in LDP-based FL and has little impact on the target model’s accuracy. We conclude that LDP-based FL needs further improvements to defend against sample reconstruction attacks effectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Locally repairable codes from combinatorial designs

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Science China(Information Sciences) 2020年第2期63卷 174-188页

作者： Yu ZHANG Haibin KAN Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Engineering Research Center of Blockchain Shanghai Institute for Advanced Communication and Data Science

Locally repairable codes（LRCs） were proposed to reduce the repair degree in distributed storage systems. In particular, LRCs with availability are highly desirable for distributed storage systems, since this kind of codes provide the mechanism of local repair for code symbols and parallel reading of hot *** this paper, we propose four types of（n, k, r, t）;LRCs from combinatorial designs. We introduce several constructions of LRCs with strict availability and some constructions of distance-optimal LRCs with information-symbol locality. Most of our constructions in this paper are over F;, i.e., they are suitable for implementation.

关键词： locally repairable codes erasure codes combinatorial designs distributed storage systems codes with availability

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