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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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RIPPB: A Robust and Improved PBFT Protocol for Blockchain

RIPPB: A Robust and Improved PBFT Protocol for Blockchain

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meng Ding Heng He Rui Qiao Xiaohu Zhou School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Computing Engineering and Built Environment Birmingham City University Birmingham UK

ISBN: (纸本)9781665409858

In this paper, we propose RIPPB, a robust and improved PBFT protocol for Blockchain, to improve the quality of service for blockchain applications. At RIPPB, we introduce reputation for nodes and use reputation as the voting weight for node consensus to go against Sybil attacks. And we design a novel improved logistic regression model to assign reputations to nodes based on their behavior involved in block generation. With this model, nodes performing Stake-grinding attacks and Bribery attacks, face high reputation penalties, which is unacceptable for malicious nodes to reduce the probability of launching these attacks. In addition, RIPPB adds a pre-commit phase to the PBFT algorithm to reduce the number of inter-node communications, thus improving the performance of the protocol. Cost-of-attack analysis shows the high robustness of our protocol. Experimental results show a 28% improvement in the throughput of our protocol over PBFT.

关键词： Industrial electronics Protocols Quality of service Throughput Robustness Blockchains Behavioral sciences

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Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical Systems

arXiv

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Feature selection method for 3d terrain model based on profile recognition and polygon-breaking algorithm

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ICIC Express Letters 2013年第3 A期7卷 741-746页

作者： Zhang, Huijie Ma, Zhiqiang Ren, Guangxu Wu, Di Yang, Fengqin School of Computer Science and Information Technology Jilin Universities Northeast Normal University No. 2555 Jingyue Street Changchun 130117 China Key Laboratory of Intelligent Information Processing Jilin Universities Northeast Normal University No. 2555 Jingyue Street Changchun 130117 China Aviation University of Air Force Changchun 130022 China

This paper presents a framework to support feature visualization in 3D terrain roaming. In this framework, feature points are redefined based on the target points of profile recognition and polygon-breaking algorithm, combined with connotative hierarchical structure. The diamond neighborhood space is proposed to decrease the computation complexity of feature selection. Additionally, feature connection and polygon breaking have been completed in order to build the whole framework. Finally, experiments have been conducted on the GTOPO30 terrain models. As a result, our framework can meet real time feature visualization and the quality of skeleton feature selection is good as a whole. By further test, the frame rate of terrain roaming with feature selection is satisfied. © 2013 ICIC International.

关键词： Visualization

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Color PNG Image Authentication Scheme Based on Rehashing and Secret Sharing Method

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Journal of information Hiding and Multimedia Signal processing 2015年第3期6卷 523-533页

作者： Lyu, Wan-Li Chang, Chin-Chen Wang, Feng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou Fujian350108 China

An authentication scheme that uses rehashing and secret sharing methods to verify the reality of a color image’s content is proposed in this paper. First, the proposed scheme uses Shamir’s secret sharing method to share image features. Second, the proposed scheme innovatively utilizes the rehashing method to overcome easy collisions of the random numbers to enhance the effectiveness against attacks. Third, the proposed method utilizes Portable Network Graphics images to hide authentication messages. Using the rehashing method, the collisions take up little in address space, making the process suitable for processing authentication messages. The experimental results indicated that the proposed scheme had very low false acceptance and rejection ratios and verified that the rehashing method can be used in image authentication applications. © 2009 ISSN 2073-4212.

关键词： Authentication

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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A Defense Framework for Backdoor Attacks in Federated Learning Based on Client-Server Detection

A Defense Framework for Backdoor Attacks in Federated Learni...

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Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on

作者： Ding Chen Tianrui Li Cai Yu Rasha Al-Huthaifi Wei Huang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Sichuan Provincial Key Laboratory of Cyberspace Security School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Cybersecurity Chengdu University of Information Technology Chengdu China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Addressing the challenge of backdoor attacks in federated learning (FL), various defense schemes are examined, resulting in the proposal of a comprehensive strategy that incorporates both client-side and server-side measures. This defense strategy is based on Dropout and LOF (Local Outlier Factor) outlier detection techniques. The model's resilience against data poisoning is enhanced by applying the Dropout technique on the client side. At the same time, the LOF outlier detection algorithm is utilized on the server side to identify potential abnormal model updates. Experimental results highlight the effectiveness of the proposed strategy, demonstrating a significant reduction in the Attack Success Rate (ASR) from 88.11% to 1.62% on the FMNIST dataset, and from 95.45% to only 0.25% on the EMNIST dataset. The success rate of backdoor attacks is notably diminished, confirming the strategy's strong backdoor defense capabilities.

关键词： Resistance Federated learning Service robots Data models Stability analysis Real-time systems Servers Anomaly detection Thermal stability Resilience

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Hyperspectral light field stereo matching

arXiv

引用

arXiv 2017年

作者： Zhu, Kang Xue, Yujia Fu, Qiang Kang, Sing Bing Chen, Xilin Yu, Jingyi School of Information Science and Technology ShanghaiTech University Shanghai China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Plex-VR Inc. Shanghai Microsoft Research RedmondWA United States Department of Computer and Information Sciences University of Delaware NewarkDE United States

In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 × 6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues that rely on BWNCC. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses. Copyright © 2017, The Authors. All rights reserved.

关键词： Cameras

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning information leakage Malware Security Risk management

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