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An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

Security and Communication Networks 2023年第1期2023卷

作者： Li, Chao Fu, Yuhan Zhang, Rui Liang, Hai Wang, Chonghua Li, Junjian Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China COMAC Shanghai Aircraft Manufacturing Co. Ltd Beijing100005 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. © 2023 Chao Li et al.

关键词： Internet of things

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Self-Enhanced Oxidation of Fluoroquinolone Antibiotics by Cuii/Peroxymonosulfate: Copper Coordination Mechanism with Antibiotics

SSRN

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SSRN 2024年

作者： Zhao, Qing Chen, Long Wang, Zhiqiang Gong, Xueqing Zhou, Lei Xiu, Guangli Shanghai Environmental Protection Key Laboratory for environmental standard and risk management of chemical pollutants School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Centre for Computational Chemistry Research Institute of Industrial Catalysis School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai200237 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

Trace copper ions are commonly found in different water bodies. They can effectively promote the degradation of various pollutants by peroxymonosulfate (PMS). However, the underlying mechanism of copper's action has not yet been clarified. Fluoroquinolone antibiotics (FQs), which are commonly used in humans and animals, often coexisting with copper metal in environmental waters, especially in aquaculture and medical wastewater. The presence of CuII under neutral conditions is found to significantly enhance the degradation efficiency of FQs by PMS. The coordination of CuII with FQs plays a crucial role in this process. Taking pefloxacin (PE), one typical FQs as an example, the structure of the copper complexes was determined using UV-visible spectroscopy, fluorescence spectroscopy, and DFT calculations as [Cu (PE) (H2O)4]2+. Comprehensive quenching experiments and electron paramagnetic resonance (EPR) test indicated that high-valence copper was responsible for the accelerated degradation. The combined results, including PMS decay and DFT calculations, indicated that CuII is initially reduced to CuI at a relatively slow rate and then rapidly oxidized to CuIII in the presence of PMS. The results highlight the valence transition of CuII/CuI/CuIII experienced during PMS activation, and the crucial role of contaminant-metal coordination in the valence transformation of copper. © 2024, The Authors. All rights reserved.

关键词： Copper compounds

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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Data Driven control and Learning systems (DDCLS)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression Visualization Laser radar Simultaneous localization and mapping Conferences Robot vision systems Measurement uncertainty

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Boosting Black-Box Adversarial Attacks with Meta Learning

arXiv

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arXiv 2022年

作者： Fu, Junjie Sun, Jian Wang, Gang The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

Deep neural networks (DNNs) have achieved remarkable success in diverse fields. However, it has been demonstrated that DNNs are very vulnerable to adversarial examples even in black-box settings. A large number of black-box attack methods have been proposed to in the literature. However, those methods usually suffer from low success rates and large query counts, which cannot fully satisfy practical purposes. In this paper, we propose a hybrid attack method which trains meta adversarial perturbations (MAPs) on surrogate models and performs black-box attacks by estimating gradients of the models. Our method uses the meta adversarial perturbation as an initialization and subsequently trains any black-box attack method for several epochs. Furthermore, the MAPs enjoy favorable transferability and universality, in the sense that they can be employed to boost performance of other black-box adversarial attack methods. Extensive experiments demonstrate that our method can not only improve the attack success rates, but also reduces the number of queries compared to other methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

引用

IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

arXiv

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Xue, Shibei Zhang, Guofeng State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises. © 2022, CC BY.

关键词： Optimization

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Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear Positive Semi-Markov Jump systems with Time-varying Delays

Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear P...

引用

Chinese control Conference (CCC)

作者： Chao Ma Hang Fu Yidao Ji Wei Wu School of Mechanical Engineering University of Science and Technology Beijing China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

In this paper, the $\mathcal{L}_{1}$ -gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design approach. More precisely, the mode-dependent... 详细信息

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper, the $\mathcal{L}_control$ -gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design approach. More precisely, the mode-dependent filters are designed in terms of practical observed modes instead of true system modes, such that less conservatism can be achieved. In addition, the effect of time-varying delays is taken into account for more robustness and applicability. By selecting suitable stochastic Lyapunov-Krasovskii functions and applying the linear programming method, sufficient conditions are established to fulfill the desired $\mathcal{L}_control$ -gain performance. Eventually, the illustrative simulation is performed to verify the effectiveness of our developed control scheme.

关键词： Maximum likelihood detection Sufficient conditions Filtering Hidden Markov models Stochastic processes Nonlinear filters Linear programming

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Nonlinear E-field Simulation of HVDC Converter Transformer Busing and Its Structure Optimization With RBF Neural Network and NSGA-II Algorithm

Nonlinear E-field Simulation of HVDC Converter Transformer B...

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IEEE International Conference on Power and Energy systems (ICPES)

作者： Yongsheng He Shiling Zhang Zongxiang Lu Pengbo Wang State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing China State Key Lab of Control and Simulation of Power Systems and Generation Equipment Haidian China Dept name of State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing China

Static DC and PR are typical operation conditions of HVDC converter transformer bushing outlet device, moreover, the electrical parameters of RIP, transformer oil and OIP have the nonlinear relationship with E-field stress and temperature. Under the temperature gradient, the electrical parameters will modify the static DC and PR E-field distribution of the composite insulation. In order to simulate static DC and PR electric field of the composite insulation structure, this paper established the full model of the converter transformer outlet. Then, the structure parameters of converter transformer outlet have been optimized using combined RBF neural network and NSGA-II algorithm. Moreover, electric field verification calculation has been carried out under AC and PR conditions. The results show that under uniform temperature condition, E-field strength is larger than that under iso-thermal condition. The overshoot coefficient of the bushing end and barrier reach 65% and 45%, respectively. The study can provide reference for the design of outlet structure of the converter transformer.

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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

arXiv

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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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