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检索条件"任意字段=19th International Conference on Control Systems and Computer Science"

共 42756 条记录，以下是51-60 订阅

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Balancing Privacy and Utility in Multivariate Time-Series Classification 19th

Balancing Privacy and Utility in Multivariate Time-Series ...

引用

19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer Security, ESORICS 2024

作者： Roman, Adrian-Silviu Genge, Béla Haller, Piroska George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Mures Targu Mures540142 Romania

ISBN: (纸本)9783031823480

In the modern era, characterized by the widespread presence of sensor-based systems, ensuring time-series data privacy without compromising utility has emerged as a significant challenge. While current strategies for safeguarding time-series data prioritize either input data protection or privacy-preserving classification models, they often fall short in assessing the balance between data privacy and utility, particularly when strong adversary classification models are involved. Our approach introduces a novel protection technique specifically designed for multivariate Time-Series Classification. this technique involves perturbing the data by distributing the noise among features using a feature importance-based approach, thus securing the data while preserving its analytical value. We propose a dual-model evaluation system consisting of two supervised classifiers, a Privacy-Breaking Classifier and a Utility-Focused Classifier. these are designed to respectively assess the potential for privacy breaches and the extent of data utility preservation in the context of protected time-series data. the experimental results demonstrate the effectiveness and viability of our methodology. Our approach provides a framework for evaluating the privacy and utility levels of time-series data. Additionally, it guides the selection of an appropriate perturbation level to ensure both aspects are adequately addressed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Investigating the Regularization of Deep Neural Networks for Affect Recognition with Relevance-Guided Local Explanations 19th

Investigating the Regularization of Deep Neural Networks f...

引用

19th international conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

作者： Rieger, Ines Chair for Cognitive Systems University of Bamberg Bamberg Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany

ISBN: (纸本)9783031815416

Deep neural networks (DNNs) have demonstrated remarkable performance in various computer vision tasks. However, they face challenges that can inhibit their performance and transparency such as the learning of spurious patterns and a lack of explanatory power. this paper addresses these challenges in the domain of affect recognition, particularly for facial expressions. Our first contribution focuses on the integration of domain-specific knowledge into DNNs. To achieve this, we improve on a regularization method that constrains class co-occurrences, thereby outperforming existing state-of-the-art approaches. Our second contribution evaluates the impact of this regularization by employing an adapted explainable AI (XAI) method that incorporates expert knowledge. the results reveal that the regularization term encourages the learning of more generalized features. Consequently, XAI methods enhance the transparency of DNNs, contributing to the development of more reliable AI systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Neural Networks Explainable AI Regularization

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Optimized Method for Dynamic Allocation of Driving Rights and Trajectory Tracking under Human-Machine Co-driving 24

Optimized Method for Dynamic Allocation of Driving Rights an...

引用

8th international conference on computer science and Artificial Intelligence, CSAI 2024

作者： Li, Hao An, Yisheng Li, Ting Li, Nandong Zhang, Yukun Yang, Peng Gao, Zhengge Chang’an University ShaanXi Xi’an China

ISBN: (纸本)9798400718182

To address human-machine conflicts caused by high driver control authority, this paper proposes a driver-vehicle model and an autonomous steering controller using Model Predictive control (MPC). Simulink simulations show that the proposed controller significantly reduces conflicts when the driver has substantial control authority. Additionally, a dynamic weight distribution model is introduced to optimize driving authority allocation by considering trajectory accuracy, stability, and driver status. the model is validated through simulations, demonstrating strong robustness and adaptability across different driver states and road conditions. © 2024 Copyright held by the owner/author(s).

关键词： Predictive control systems

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19th international conference on Information Security Practice and Experience, ISPEC 2024

19th International Conference on Information Security Practi...

引用

19th international conference on Information Security Practice and Experience, ISPEC 2024

ISBN: (纸本)9789819790524

the proceedings contain 22 papers. the special focus in this conference is on Information Security Practice and Experience. the topics include: Lattice-Based Universal Designated Multi-verifiers Signature Scheme;Differential Cryptanalysis Against SIMECK Implementation in a Leakage Profiling Scenario;shrinkable Ring Signatures: It Wasn’t them!;Related-Tweakey Boomerang and Rectangle Attacks on Reduced-Round Joltik-BC;isogeny-Based Password-Authenticated Key Exchange Based on Shuffle Algorithm;a New Construction of Leakage-Resilient Identity-Based Encryption Scheme;enhancing Portability in Deep Learning-Based Side-Channel Attacks Against Kyber;Generic CCA Secure Key Homomorphic KEM and Updatable Public Key Encryption;Breaking GEA-Like Stream Ciphers with Lower Time Cost;SNIPER: Detect Complex Attacks Accurately from Traffic;from the Perspective of Prototypes: A Privacy-Preserving Personalized Federated Learning Framework;efficient Large-Scale Multi-party Computation Based on Garbled Circuit;Parallel Implementation of Sieving Algorithm on Heterogeneous CPU-GPU Computing Architectures;Unveiling the Efficacy of BERT’s Attention in Memory Obfuscated Malware Detection;An eID-Based Privacy-Enhanced Public Transportation Ticket System;outsourced and Robust Multi-party Computation with Identifying Malicious Behavior and Application to Machine Learning;controlled Multi-client Functional Encryption for Flexible Access control;a Secure Incentive Mechanism in Blockchain-Based Mobile Crowdsensing;a Comparative Analysis of Phishing Tools: Features and Countermeasures;OEIBS: A Secure Obfuscation for Encrypted Identity-Based Signatures Scheme in NB-IoT.

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A Framework for Applying Digital Twins to Support Incident Response 19th

A Framework for Applying Digital Twins to Support Incident...

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作者： Suhail, Sabah Iqbal, Mubashar McLaughlin, Kieran Lee, Brian Imtiaz, Babar Queen’s University Belfast Belfast United Kingdom University of Tartu Tartu Estonia Technological University of the Shannon Athlone Ireland

ISBN: (纸本)9783031823480

the convergence of information technology (IT) and operational technology (OT) has made manufacturing industries an attractive target for cyberattacks, ranging from industrial espionage to sabotage. Existing security tools, operating alone, are not capable enough to manage cybersecurity operations effectively. Digital twin (DT), as a security-enhancing enabler, can support complementary security measures alongside existing incident response (IR) solutions. DTs have been proposed in different IR phases;however, a comprehensive solution covering the IR lifecycle has yet to be addressed. this paper presents a DT-based IR solution to guide plant operators in modeling security-enhancing DTs for manufacturing industries. Moreover, the DT-based IR solution integrates existing security tools to ensure the effective safeguarding of critical assets and prompt response to cyber incidents. With an automotive assembly line as a cyber-physical production system (CPPS) use case, we examine the applicability of a DT-based IR solution. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber-Physical Production System (CPPS) Cybersecurity Digital Twin (DT) Incident Response (IR) Industrial control System (ICS)

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Fractional-order Adaptive Sliding Mode Projective Synchronization of Different Hyperjerk systems 13

Fractional-order Adaptive Sliding Mode Projective Synchroniz...

引用

13th international conference on Intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Baojie Liu, Changchao Zhu, Zhiyuan Feng, Yuming Cao, Longzhou College of Computer Science and Engineering Chongqing Three Gorges University Chongqing China

ISBN: (纸本)9798331516147

this paper presents an adaptive fractional order sliding mode projective synchronization scheme for different hyperjerk systems with uncertainty and disturbances. A fractional order sliding mode controller with three fractional terms is proposed for the projective synchronization of different hyperjerk systems. the uncertainty and disturbance is considered with adaptive scheme. Numerical simulation verifies the effectiveness and feasibility of the control method. © 2025 IEEE.

关键词： Sliding mode control

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AdaLightLog: Enhancing Application Logs Anomaly Detection via Adaptive Federating Learning 19th

AdaLightLog: Enhancing Application Logs Anomaly Detection vi...

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19th international conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Menegatti, Danilo De Santis, Emanuele Felli, Stefano Giuseppi, Alessandro Department of Computer Control and Management Engineering ‘Antonio Ruberti’ Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842597

the role of anomaly detection systems in Critical Infrastructures (CIs) is critical due to the complexity of CIs and their control systems, which are usually implemented by computer-based controllers that constantly produce logs of their activities. Moreover, many CIs, located in different locations or even belonging to different companies, may share similar application software for controlling the CIs themselves. the goal of this work is to use such logs to perform automatic anomaly detection in a federated learning (FL) paradigm, which ensures that no data is exchanged between sites to train the anomaly detection models, but each learning agent learns on its own data, leveraging the knowledge acquired by the other agents. Our proposed approach - AdaLightLog - which implements a modified FL paradigm with adaptive loss functions at local servers side and weighted averaging of local server models, so to differentiate the quality of the different local servers’ models in the global averaging, is tested against state-of-the-art methods and shows an improvement in performance in terms of accuracy, precision and recall with respect to the standard FL implementation (FedAvg). Furthermore, a comparison between different metrics for the adaptive loss functions and the dynamic weights is presented. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Exploring Anomaly Detection for Marine Radar systems 19th

Exploring Anomaly Detection for Marine Radar Systems

引用

作者： Saillard, Antoine Wolsing, Konrad Wehrle, Klaus Bauer, Jan Cyber Analysis and Defense Fraunhofer FKIE Wachtberg Germany Communication and Distributed Systems RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031823480

Marine radar systems are a core technical instrument for collision avoidance in shipping and an indispensable decision-making aid for navigators on the ship’s bridge in limited visibility conditions at sea, in straits, and harbors. While electromagnetic attacks against radars can be carried out externally, primarily by military actors, research has recently shown that marine radar is also vulnerable to attacks from cyberspace. these can be carried out internally, less "loudly", and with significantly less effort and know-how, thus posing a general threat to the shipping industry, the global maritime transport system, and world trade. Based on cyberattacks discussed in the scientific community and a simulation environment for marine radar systems, we investigate in this work to which extent existing Intrusion Detection System (IDS) solutions can secure vessels’ radar systems, how effective their detection capability is, and where their limits lie. From this, we derive a research gap for radar-specific methods and present the first two approaches in that direction. thus, we pave the way for necessary future developments of anomaly detection specific for marine navigation radars. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marine radar

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Securing the Silver Surfers: Cybersecurity Awareness Among Older Adults 19

Securing the Silver Surfers: Cybersecurity Awareness Among O...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ahmad Nawi, Haslinda Sutan Omar, Siti Fatimah Basaruddin, Suzana Info. Science & Computing Department Management and Science University Selangor Malaysia Software Engineering Section UniKL MIIT Kuala Lumpur Malaysia Informatics & Analytics Section UniKL MIIT Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

this research investigates the perceptions of cybersecurity among older adults in Malaysia, aged 60 years and above, who are reliant on technology for basic activities. the data from 331 participants showed that 30.8% were online for 4-8 hours daily, while 3.3% were completely disconnected. Interestingly, more than half of the respondents were unaware of cybersecurity threats, especially malware and ransomware, which put their data in jeopardy. While there is some awareness related to unauthorized access and computer fraud, knowledge gaps remain significant, and effective educational interventions are thus needed. Informal, self-directed learning appears to be a widespread way of acquiring skills in this age group, often supplemented by community workshops and peer networks. Given that almost half of the older adults spend considerable time online, improving their digital skills in tandem with their support systems is an important goal for enhancing their online experiences. It indicates the need for a twin approach: enhancing cybersecurity awareness with specialized educational programs, alongside developing flexible opportunities for informal learning. Educational institutions and relevant policies are encouraged to prioritize building hands-on, specific cybersecurity education for seniors. Also, user interface design considering cybersecurity-related issues can make using the technological environment as user-friendly and secure as possible. these strategies will contribute to creating an inclusive digital society, where older adults are confident and safe in using digital technologies while continuously contributing to their personal and professional development. © 2025 IEEE.

关键词： Economic and social effects

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Remote Sensing Data for Predicting Crop Growth 19th

Remote Sensing Data for Predicting Crop Growth

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19th international conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

作者： Li, Yunan Dhelim, Sahraoui Chen, Liming Kechadi, M-Tahar Belfield Dublin 4 Dublin Ireland School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9783031815416

this paper introduces an efficient approach, the dynamic coefficient polynomial model, which emulates crop growth dynamics using NDVI. this model, a significant improvement over traditional models like the NDVI mean and static polynomial models, is designed to be adaptable over time and incorporates spatial variables to account for the diverse growth conditions experienced in different regions. Consequently, the model’s responses and adaptations are influenced by the specific crop growth dynamics observed within these spatial dimensions, adding a new dimension to crop growth forecasting. Our results show that the proposed model achieves a higher accuracy than the other machine learning models, which is about 90.3%. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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