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Multi-variant network address hopping to defend stealthy crossfire attack

science China(Information sciences) 2020年第6期63卷 241-243页

作者： Boyang ZHOU Gaoning PAN Chunming WU Kai ZHU Wei RUAN Zhejiang Lab College of Computer Science Zhejiang University College of Control Science and Engineering Zhejiang University

Dear editor,Recently, crossfire attack has been witnessed as a new distributed denial-of-service (DDoS) weapon that can effectively cut off the data connections between the chosen target area (wd) of servers and the end hosts (H). The attack is launched by a network of bot (botnet) to drain bandwidth of persistent routes (PRs) in the indirect and lowrate data flooding towards the decoys that are located in upstream to the target, where the PRs are

关键词： TTL Multi-variant network address hopping to defend stealthy crossfire attack

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Soft Decision Based Gaussian-Laplacian Combination Model for Noisy Speech Enhancement

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Chinese Journal of Electronics 2018年第4期27卷 827-834页

作者： OU Shifeng SONG Peng GAO Ying School of Science and Technology for Opto-electronic Information Yantai University School of Computer and Control Engineering Yantai University

One of the key issues of noisy speech enhancement technique is to achieve appropriate statistical distributions to model the clean speech and noise signals accurately. Most of the existing algorithms try to employ a sole model assumption in transform domain, which, however, has been proven to being contrary with the fact. To address this problem, the statistical properties of clean speech as well as several noise signals are analyzed using actual data in Discrete cosine transform(DCT) domain, and the study indicates the statistic of clean speech DCT coefficients tending to fall somewhere in between the Gaussian and Laplacian distribution. Based on the results,a novel speech enhancement algorithm is proposed using Gaussian-Laplacian combination model, whose core is employing a linear combination of Gaussian and Laplacian distribution to model the statistic of clean speech DCT coefficients. The corresponding weights of either distribution to the combination model are adaptively adjusted in terms of the probability of each hypothesis, which is estimated based on a soft decision technique by using Bayesian theorem. Through a number of objective and subjective tests,we compare the performance of the proposed algorithm with other recent model based approaches and have found that our algorithm is superior to the related approaches at all testing environments.

关键词： Speech enhancement Soft decision Speech distortion Combination model

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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science China(Information sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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Laplacian Speech Model and Soft Decision Based MMSE Estimator for Noise Power Spectral Density in Speech Enhancement

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Chinese Journal of Electronics 2018年第6期27卷 1214-1220页

作者： OU Shifeng SONG Peng GAO Ying School of Science and Technology for Opto-electronic Information Yantai University School of Computer and Control Engineering Yantai University

The estimation of noise Power spectral density(PSD) is a very crucial issue for speech enhancement as a result of its significant effect on the quality and intelligibility of the enhanced speech. Most of the existing estimators for noise PSD try to employ Gaussian speech priors, which, however, have been proven inconsistent with the reality. We derived an effective solution to this problem of estimating noise PSD in the Minimum mean square error(MMSE) sense when the speech component is modeled by a Laplacian distribution. Meanwhile, the soft decision technique instead of the hard Voice activity detection(VAD) is evolved into our algorithm, which can automatically makes the estimation unbiased without requiring a bias compensation. The performance of the proposed method is tested by several objective and subjective measures under various stationary and nonstationary noise environments. The results confirm that our method achieves good performance for all the noise conditions and Signalnoise-ratio(SNR) settings.

关键词： Noise PSD estimation Speech enhancement Laplacian speech model Soft decision

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Aircraft-on-ground path following control by dynamical adaptive backstepping

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Chinese Journal of Aeronautics 2013年第3期26卷 668-675页

作者： Chen Bihua Jiao Zongxia Shuzhi Sam Ge Science and Technology on Aircraft Control Laboratory Beihang University Department of Electrical & Computer Engineering National University of Singapore

The necessity of improving the air traffic and reducing the aviation emissions drives to investigate automatic steering for aircraft to effectively roll on the ground. This paper addresses the path following control problem of aircraft-on-ground and focuses on the task that the aircraft is required to follow the desired path on the runway by nose wheel automatic steering. The proposed approach is based on dynamical adaptive backstepping so that the system model does not have to be transformed into a canonical triangular form which is necessary in conventional backstepping design. This adaptive controller performs well despite the lack of information on the aerodynamic load and the tire cornering stiffness parameters. Simulation results clearly demonstrate the advantages and effectiveness of the proposed approach.

关键词： Adaptive control Aircraft-on-ground Backstepping Nonlinear dynamical systems Path following

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An Improved Evolvable Oscillator and Basis Function Set for control of an Insect-Scale Flapping-Wing Micro Air Vehicle

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Journal of computer science & Technology 2012年第5期27卷 966-978页

作者： John C.Gallagher Michael W.Oppenheimer Department of Computer Science and Engineering Wright State University Control Sciences Branch Air Force Research Laboratory

This paper introduces an improved evolvable and adaptive hardware oscillator design capable of supporting adaptation intended to restore control precision in damaged or imperfectly manufactured insect-scale flapping-wing micro air vehicles. It will also present preliminary experimental results demonstrating that previously used basis function sets may have been too large and that significantly improved learning times may be achieved by judiciously culling the oscillator search space. The paper will conclude with a discussion of the application of this adaptive, evolvable oscillator to full vehicle control as well as the consideration of longer term goals and requirements.

关键词： evolvable and adaptive hardware micro air vehicle evolutionary algorithm

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SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Qiao, Sibo Guo, Qiang Shi, Fengdong Wang, Min Zhu, Haohao Khan, Fazlullah Rodrigues, Joel J.P.C. Lyu, Zhihan School of Software Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China National Demonstration Center for Experimental Engineering Training Education Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China School of Control Sciences and Engineering Tiangong University Tianjin300387 China School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China Higher School of Technology Amazonas State University Amazonas Manaus69050-010 Brazil Department of Game Design Faculty of Arts Uppsala University Uppsala Sweden

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems. © 2013 IEEE All rights reserved.

关键词： Information leakage

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Optimization techniques for modeling with piecewise-linear functions

arXiv

引用

arXiv 2025年

作者： Dobrovoczki, Péter Kis, Tamás Engineering and Management Intelligence Research Laboratory HUN-REN Institute for Computer Science and Control Kende utca 13-17 Budapest1111 Hungary Department of Operations Research Institute of Mathematics Eötös Loránd University Pázmány Péter sétány 1/C Budapest1117 Hungary

In this paper we aim to construct piecewise-linear (PWL) approximations for functions of multiple variables and to build compact mixed-integer linear programming (MILP) formulations to represent the resulting PWL function. On the one hand, we describe a simple heuristic to iteratively construct a triangulation with a small number of triangles, while decreasing the error of the piecewise-linear approximation. On the other hand, we extend known techniques for modeling PWLs in MILPs more efficiently than state-of-the-art methods permit. The crux of our method is that the MILP model is a result of solving some hard combinatorial optimization problems, for which we present heuristic algorithms. The effectiveness of our techniques is demonstrated by a series of computational experiments including a short-term hydropower scheduling problem. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer linear programming

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

arXiv

引用

arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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