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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Improving Machine Learning Performance for Diabetes Prediction

Improving Machine Learning Performance for Diabetes Predicti...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2023

作者： Benabderrahmane, Jawad Kasri, Mohammed El Guabassi, Inssaf El Ansari, Anas Beni-Hssane, Abderrahim Laroseri Laboratory Computer Science Department Faculty of Sciences Chouaib Doukkali University El Jadida Morocco MASI Laboratory Mohammed First University Nador Morocco High School of Artificial Intelligence and Data Science Ibn Zohr University Taroudant Morocco

ISBN: (纸本)9783031543173

Diabetes is a formidable ailment characterized by elevated glucose levels in the bloodstream. Hence, it can cause many complications if it remains untreated and unidentified. Machine learning algorithms play a crucial role in early-stage detection and prediction of diabetes. This research aims to enhance the accuracy of diabetes mellitus prediction by using several techniques within the realm of machine learning. In this regard, the Pima Indians Diabetes dataset was used. Then, different machine learning algorithms have been applied separately or using ensemble learning. As a result, Support Vector Machine achieved an accuracy of 77.89%. Moreover, an empirical assessment of ensemble learning methodology involving the voting classifier and Bagging is conducted alongside contemporary techniques and base classifiers. The ensemble approach demonstrates its prowess, with the voting classifier achieving a result of 90.11%. Notably, the Bagging Classifier emerges as the frontrunner, attaining a remarkable accuracy value of 96.69%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Towards a Self-contained Data-driven Global Weather Forecasting Framework 41

Towards a Self-contained Data-driven Global Weather Forecast...

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41st International Conference on Machine Learning, ICML 2024

作者： Xiao, Yi Bai, Lei Xue, Wei Chen, Hao Chen, Kun Chen, Kang Han, Tao Ouyang, Wanli Department of Computer Science and Technology Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Qinghai University and Intelligent Computing Application Laboratory of Qinghai Province China School of Information Science and Technology Fudan University Shanghai China

Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i.e., analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in numerical weather prediction centers;it is accurate but computationally expensive. In this paper, we aim to couple the AI forecasting model, FengWu, with 4DVar to build a self-contained data-driven global weather forecasting framework, FengWu-4DVar. To achieve this, we propose an AI-embedded 4DVar algorithm that includes three components: (1) a 4DVar objective function embedded with the FengWu forecasting model and its error representation to enhance efficiency and accuracy;(2) a spherical-harmonic-transform-based (SHT-based) approximation strategy for capturing the horizontal correlation of background error;and (3) an auto-differentiation (AD) scheme for determining the optimal analysis fields. Experimental results show that under the ERA5 simulated observational data with varying proportions and noise levels, FengWu-4DVar can generate accurate analysis fields;remarkably, it has achieved stable self-contained global weather forecasts for an entire year for the first time, demonstrating its potential for real-world applications. Additionally, our framework is approximately 100 times faster than the traditional 4DVar algorithm under similar experimental conditions, highlighting its significant computational efficiency. Copyright 2024 by the author(s)

关键词： Data assimilation

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PNSS: Unknown Face Presentation Attack Detection with Pseudo Negative Sample Synthesis

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computers, Materials & Continua 2025年第5期83卷 3097-3112页

作者： Hongyang Wang Yichen Shi Jun Feng Zitong Yu Zhuofu Tao School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuang050043China Shijiazhuang Key Laboratory of Artificial Intelligence Shijiazhuang Tiedao UniversityShijiazhuang050043China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai200240China School of Computer Science and Technology Great Bay UniversityDongguan523808China School of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Face Presentation Attack Detection(fPAD)plays a vital role in securing face recognition systems against various presentation *** supervised learning-based methods demonstrate effectiveness,they are prone to overfitting to known attack types and struggle to generalize to novel attack *** studies have explored formulating fPAD as an anomaly detection problem or one-class classification task,enabling the training of generalized models for unknown attack ***,conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown *** address this challenge,we propose a novel framework focusing on unknown attack detection using exclusively bonafide facial data during *** core innovation lies in our pseudo-negative sample synthesis(PNSS)strategy,which facilitates learning of compact decision boundaries between bonafide faces and potential attack ***,PNSS generates synthetic negative samples within low-likelihood regions of the bonafide feature space to represent diverse unknown attack *** overcome the inherent imbalance between positive and synthetic negative samples during iterative training,we implement a dual-loss mechanism combining focal loss for classification optimization with pairwise confusion loss as a *** architecture effectively mitigates model bias towards bonafide samples while maintaining discriminative *** evaluations across three benchmark datasets validate the framework’s superior ***,our PNSS achieves 8%–18% average classification error rate(ACER)reduction compared with state-of-the-art one-class fPAD methods in cross-dataset evaluations on Idiap Replay-Attack and MSU-MFSD datasets.

关键词： Face presentation attack detection pseudo negative sample anomaly detection one-class classification

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Observation points classifier ensemble for high-dimensional imbalanced classification

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CAAI Transactions on intelligence Technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models 38

FIRE: A Dataset for Feedback Integration and Refinement Eval...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. © 2024 Neural information processing systems foundation. All rights reserved.

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Exploring static rebalancing strategies for dockless bicycle sharing systems based on multi-granularity behavioral decision-making

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 27-43页

作者： Zhang, Chao Zhang, Jiahui Li, Wentao Castillo, Oscar Zhang, Jiayi School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China College of Artificial Intelligence Southwest University Chongqing400715 China Division of Graduate Studies and Research Tijuana Institute of Technology Tijuana22379 Mexico School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland

In the continuously evolving context of urbanization, more people flock to cities for job opportunities and an improved quality of life, resulting in undeniable pressure on transportation networks. This leads to severe daily commuting challenges for residents. To mitigate this urban traffic pressure, most cities have adopted urban dockless bicycle sharing systems (UDBSS) as an effective measure. However, making accurate decisions regarding UDBSS demand in different city locations is crucial, as incorrect choices can worsen transportation problems, causing difficulties in finding bicycles or excessive deployments leading to disorderly accumulation. To address this decision-making challenge, it is essential to consider uncertain factors like daily weather, temperature, and workdays. To tackle this effectively, we construct an adjustable multi-granularity (MG) complex intuitionistic fuzzy (CIF) information system using complex intuitionistic fuzzy sets (CIFSs). This system objectively determines classification thresholds using an evaluation-based three-way decision (TWD) method, creating adjustable MG CIF probabilistic rough sets (PRSs). Additionally, to recognize the irrationality of decision-makers (DMs), we propose a method that combines prospect theory (PT) with regret theory (RT), providing a more comprehensive understanding of the influence of DMs' psychological factors on decision outcomes. Building upon these foundations, we present static rebalancing strategies for UDBSS based on MG PRSs and prospect-regret theory (P-RT) within the CIF information system. Finally, using UDBSS data collected from various sensors, we conduct experimental analysis to verify its feasibility and stability. In summary, this approach considers residents’ daily usage preferences, including bicycles utilization and return, with the aim of minimizing unmet resident demands and predicting usage patterns for the next day. It effectively addresses the issue of UDBSS distribution inefficien

关键词： Bicycles

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Enhancing Event Tagger with Automatic Speech Recognizer for Audio Multi-task Scenarios by Distillation with Pre-Trained Large Models

Enhancing Event Tagger with Automatic Speech Recognizer for ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Cheng, Jianfeng Liu, Ye Yin, Jian Wang, Liangdao Pan, Yan Lizhi Inc. Artificial Intelligence Department Guangzhou Beijing China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the continuous expansion of robotics and digital humans in practical applications, the demand for the auditory system is becoming deeper, usually requiring more efficient speech recognition framework capabilities to handle multiple tasks and use fewer resources. In previous audio processing frameworks, each type of audio processing task typically requires constructing a standalone deep network model for training, which results in more training data and higher training time when constructing models for multi-task audio scenarios simultaneously. The recent improvement of audio models based on transformers have brought about methods that can handle multiple audio tasks concurrently. However, recent related methods still require retraining multi-task targets with an amount of data, and achieve the general effect after training for multi-task scenarios than the simple combination of standalone methods processed separately. In order to better build a model that can handle multiple audio tasks, we propose a novel framework of distillation through pre-trained large models for enhancing event tagger with automatic speech recognizer. Through multiple rounds of experiments on several audio datasets, it has been verified that the proposed framework can achieve better results than the baseline for multitasking, and comparative results with less parameters compared to the baselines for single-task scenarios. © 2024 IEEE.

关键词： Continuous speech recognition

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Energy transition and digitalization: Small modular reactors and smart grids 7

Energy transition and digitalization: Small modular reactors...

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7th IEEE International Congress on Information science and Technology, CiSt 2023

作者： Housni, Hafsa Majit, Nissrine Amrous, Naila Daoudi, Najima Malzi, Mohamed Jaoud CIAS: Competitive Intelligence Analysis and Strategy School of Information Sciences Laboratory MIKS: Mediation Information Knowledge Society Rabat Morocco Laboratory LyRICA: Computer Science Data Science and Artificial Intelligence ITQAN: Innovative Technologies Data Quality and Artificial Intelligence School of Information Sciences Rabat Morocco Research Laboratory in Energy Environment and Resource Economics Cadi Ayyad University Rabat Morocco

ISBN: (纸本)9781665461337

This article aims to critically evaluate the current state of digitalization in SGs and SMRs and explore their implications for sustainable energy systems. Herein, we have compiled a table of information sources specifically focused on the integration of digitalization in Smart Grids (SG) and Small Modular Reactors (SMRs). The selected articles were chosen based on specific eligibility, inclusion, and exclusion criteria, ensuring their relevance and quality for our analysis. By conducting an in-depth review of the application of digital technologies in the context of SGs and SMRs, key trends, challenges, and opportunities are identified. The review encompasses various aspects, including advanced sensors, data analytics, optimization techniques, communication technologies, and cybersecurity considerations. The results highlight the potential of deep learning algorithms, data-enabled physics-informed machine learning, and digital twin technology in enhancing the safety, performance, and efficiency of SMRs. Moreover, digital technologies are found to contribute to improved grid management, increased renewable energy integration, and effective demand-side management. However, challenges related to data uncertainty, quality, security, and complexity need to be addressed. The article concludes by emphasizing the transformative potential of digitalization in shaping the future of energy systems and the importance of continued research and collaboration to overcome challenges and fully realize the benefits of digital technologies in the energy transition. © 2023 IEEE.

关键词： Smart power grids

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Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection 38

Dissect Black Box: Interpreting for Rule-Based Explanations ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Yu Li, Ruoyu Wu, Nengwu Li, Qing Lin, Xinhan Hu, Yang Li, Tao Jiang, Yong Shanghai Artificial Intelligence Laboratory China Peng Cheng Laboratory China College of Computer Science and Software Engineering Shenzhen University China Tsinghua University China Tsinghua Shenzhen International Graduate School China Hunan University of Science and Technology China

In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the opaque nature of black-box anomaly detection models, which obscure the rationale behind their predictions. In this paper, we present a novel method to interpret the decision-making processes of these models, which are essential for detecting malicious activities without labeled attack data. We put forward the Segmentation Clustering Decision Tree (SCD-Tree), designed to dissect and understand the structure of normal data distributions. The SCD-Tree integrates predictions from the anomaly detection model into its splitting criteria, enhancing the clustering process with the model's insights into anomalies. To further refine these segments, the Gaussian Boundary Delineation (GBD) algorithm is employed to define boundaries within each segmented distribution, effectively delineating normal from anomalous data points. At this point, this approach addresses the curse of dimensionality by segmenting high-dimensional data and ensures resilience to data variability and perturbations through flexible boundary fitting. We transform the intricate operations of anomaly detection into an interpretable rule's format, constructing a comprehensive set of rules for understanding. Our method's evaluation on diverse datasets and models demonstrates superior explanation accuracy, fidelity, and robustness over existing method, proving its efficacy in environments where interpretability is paramount. © 2024 Neural information processing systems foundation. All rights reserved.

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