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Counteracting New Attacks in CPS: A Few-shot Class-incremental Adaptation Strategy for Intrusion Detection System

IEEE Transactions on Network and Service Management 2025年

作者： Dong, Xinrui Lai, Yingxu Zhang, Xiao Xu, Xinyu Beijing University of Technology College of Computer Science Beijing100124 China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing100124 China

The deep integration of physical devices and communication networks has increased the security risks of cyber-physical systems (CPSs) compared to traditional control systems. Deep learning-based intrusion detection systems (IDSs) play a crucial role in ensuring CPSs security. However, the existing IDSs often rely on known attack features, rendering them unable to withstand emerging new attacks arising from the dynamic evolution of intrusion behaviors. This paper aims to develop an IDSs with high adaptability and strong generalization capabilities, which is capable of rapidly adapting to new attack classes with only a few new samples. To achieve this objective, we propose CAT-IDS, a few-shot class-incremental adaptation strategy for an IDS to counteract new attacks on CPSs. We design a highly symmetric classifier structure for CAT-IDS that can flexibly adjust the classification space to adapt to new attacks. Furthermore, we calibrate the biased distribution formed by a few training samples through statistical feature transfer. In order to prevent the model from forgetting old attack information during the adaptation process, we devise hybrid features for attack detection. These features contain essential information for both old and new class classifications. We demonstrate the effectiveness of CAT-IDS through multiple experiments on three CPSs datasets. The results show that CAT-IDS achieves an average accuracy improvement of approximately 4. 5% compared to the state-of-the-art methods, demonstrating its superior ability to adapt to new attacks while maintaining high performance in classifying existing attacks. © 2004-2012 IEEE.

关键词： Network intrusion

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Anti-windup for Internal Model control with Asymmetric Input Saturation

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IEEE control Systems Letters 2025年 9卷 126-131页

作者： Adegbege, Ambrose A. Owolabi, Opeyemi S. Ogunba, Kolawole S. Jubril, Abimbola M. The College of New Jersey Laboratory for Embedded Control and Optimization (LECO) Department of Electrical and Computer Engineering Ewing 08628-0718 NJ United States Obafemi Awolowo University Department of Electronic and Electrical Engineering Ile-Ife Nigeria

In this paper, we introduce a novel anti-windup design approach for internal model control (IMC) that addresses the issue of asymmetric input saturation. To enhance closed-loop performance during periods of saturation, we utilize information about the asymmetrical limits of input saturation in the anti-windup design. This is achieved through a combination of static matrix scaling, a constant offset term, and a generalized sector condition that guarantees both global and regional stability of the closed loop. Our approach generalizes existing techniques for incorporating directionality compensation into anti-windup designs and can be interpreted as a search over a class of left-coprime factorization of the linear internal model controller. We demonstrate the effectiveness of this design approach on a well-known and widely used anti-windup example. © 2017 IEEE.

关键词： Anti-windup Synthesis Asymmetric Saturation Constrained control Internal Model control

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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CASI: Context-aware Automatic Semantic Inference by fusing video and network traffic information in industrial control systems

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Information Fusion 2025年 122卷

作者： Hetu, Zheqiu Zhang, Zhenyong Wang, Mufeng Xv, Zidong Meng, Jie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China State Key Laboratory of Industrial Control Technology and College of Control Science and Engineering Zhejiang University Hangzhou310027 China

The increasing adoption of Internet of Things devices in Industrial control Systems (ICS) has provided both attackers and security professionals with new perspectives on these traditionally safety-critical systems. Our observations reveal that video captured by field-deployed cameras contains process variables (PVs) essential for both ICS attack and defense. Based on this insight, we propose a Context-Aware Semantic Inference (CASI) method. CASI integrates video information from cameras with network traffic to infer the physical semantics of PVs in network packets, serving as a tactical tool for attackers or security professionals. A key advantage of CASI is its non-intrusive nature, deriving PVs location in the packets and semantics solely from video and network traffic without interacting with the runtime ICS, which is both covert and ensures the uninterrupted operation of the system. We constructed an ICS testbed using real devices to replicate a simplified industrial process, collecting traffic from 2 industrial protocols and creating 4 datasets. We evaluated CASI's precision and recall in detecting PVs within this context, comparing it with similar tools. Experimental results demonstrate that CASI achieves a recall rate of 1.00 in PV semantic extraction, significantly outperforming other tools. Furthermore, leveraging CASI-derived semantic information for data tampering attacks successfully disrupted monitoring and data acquisition programs, posing a severe security threat to the ICS. © 2025 Elsevier B.V.

关键词： Reverse engineering

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Geometric Reachability for Attitude control Systems via Contraction Theory

arXiv

引用

arXiv 2025年

作者： Xu, Chencheng Jafarpour, Saber Zhao, Chengcheng Shi, Zhiguo Chen, Jiming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States College of Control Science and Engineering Zhejiang University Hangzhou China

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold SO(3) × 3 and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the Riemannian distance between nearby trajectories of the attitude control systems. By combining these trajectory bounds with numerical simulations, we provide a simulation-based algorithm to over-approximate the reachable sets of attitude systems. We show that the search for optimal metrics for distance bounds can be efficiently performed using semidefinite programming. Additionally, we introduce a practical and effective representation of these over-approximations on manifolds, enabling their integration with existing Euclidean tools and software. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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Distill & Contrast: A New Graph Self-Supervised Method With Approximating Nature Data Relationships

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3284-3297页

作者： He, Dongxiao Zhao, Jitao Guo, Rui Feng, Zhiyong Huo, Cuiying Jin, Di Pedrycz, Witold Zhang, Weixiong Tianjin University College of Intelligence and Computing Tianjin300350 China Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Hong Kong Polytechnic University Department of Data Science and Ai Hong Kong

Contrastive Learning (CL) has emerged as a popular self-supervised representation learning paradigm that has been shown in many applications to perform similarly to traditional supervised learning methods. A key component of CL is mining the latent discriminative relationships between positive and negative samples and using them as self-supervised labels. We argue that this discriminative contrastive task is, in essence, similar to a classification task, and the "either positive or negative"hard label sampling strategies are arbitrary. To solve this problem, we explore ideas from data distillation, which considers probabilistic logit vectors as soft labels to transfer model knowledge. We attempt to abandon the classical hard sampling labels in CL and instead explore self-supervised soft labels. We adopt soft sampling labels that are extracted, without supervision, from the inherent relationships in data pairs to retain more information. We propose a new self-supervised graph learning method, Distill and Contrast (D&C), for learning representations that closely approximate natural data relationships. D&C extracts node similarities from the features and structures to derive soft sampling labels, which also eliminate noise in the data to increase robustness. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method. © 2025 IEEE.

关键词： Self-supervised learning

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Business Process Representation Based on Graph Convolutional Network 1st

Business Process Representation Based on Graph Convolutiona...

引用

1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Zeng, Qingtian Zhang, Lin Cao, Rui Guo, Wenyan Li, Chao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao Shandong China School of Information and Control Engineering Qingdao University of Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789819794119

The effective representation of business processes is a key problem of predictive monitoring based on deep learning networks. Most of the existing business process representation methods are unable to accurately reflect the characteristics and node information aggregation of events in the graph structure. Therefore, a business process representation method based on graph convolutional network is proposed. Firstly, the diagram structure is constructed based on the same prefix window event of the business process. Secondly, on the basis of the constructed graph structure data, the node vectorization representation based on graph convolutional network model is given, and the information is aggregated through the graph convolutional network. Then, the depth representation of nodes is applied to the downstream prediction task of the business process. Finally, five real datasets are used for experimental evaluation. The experimental results show that the proposed event log graph construction strategy and process representation method are effective in business process prediction. The source code of this work is available at https://***/LinZhang0/GCNp. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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