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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Automated and non-invasive UAV-based system for the monitoring and the group size estimation of dolphins 7

Automated and non-invasive UAV-based system for the monitori...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Dimauro, Giovanni Simone, Lorenzo Carlucci, Roberto Fanizza, Carmelo Lomonte, Nunzia Maglietta, Rosalia University of Bari 'Alda Mora' Department of Computer Science Bari Italy University of Pisa Department of Computer Science Pisa Italy University of Bari 'Alda Mora' Department of Biology Bari Italy Jonian Dolphin Conservation Taranto Italy Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing Bari Italy

ISBN: (纸本)9789532901160

Unmanned Aerial Vehicles (UAVs or drones) have been extensively applied in monitoring studies to address marine and terrestrial wildlife animals. The main advantages of UAV s are their operational flexibility and low cost. In the last few years, the need to safeguard and protect the population of cetaceans in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea) has grown in importance, and the UAVs video-analysis systems meet this need. In particular, dolphins, which can be potentially harmed by human activities in this area, could benefit a lot from the use and the application of automatic systems, in terms of estimation of group size and abundance. The goal of this study is to develop an automated non-invasive system for the analysis of videos acquired by UAV s, devoted to the estimation of cetaceans group size and abundance. An automated system like the proposed one would allow having an immediate estimate of the number of the encountered cetaceans, onboard the vessel, to be compared with the estimation made by the Marine Mammal Observer (MMO) in charge or it would be a useful support system for the expert when he has to analyze the video in the laboratory. In this paper, as a case of study, the UAV s video-analysis system has been applied on videos acquired by a camera mounted on a drone, during surveys in the Gulf of Taranto. Two different models of machine learning were used, and both the trained models are able to solve the task well albeit with some limitations. © 2022 University of Split, FESB.

关键词： Drones

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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Data, Decision and systems (ICDDS), IEEE International Conference on

作者： Rajendra Hegadi Syam Sankar Karthik Akella Koushik Reddy Pavan Kumar C Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology.

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Real-Time Human Fall Detection using a Lightweight Pose Estimation Technique

arXiv

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arXiv 2024年

作者： Alam, Ekram Sufian, Abu Dutta, Paramartha Leo, Marco Department of Computer Science Gour Mahavidyalaya West Bengal India Department of Computer Science University of Gour Banga India Department of Computer and System Sciences Visva-Bharati University India National Research Council of Italy Institute of Applied Sciences and Intelligent Systems Lecce73100 Italy

The elderly population is increasing rapidly around the world. There are no enough caretakers for them. Use of AI-based in-home medical care systems is gaining momentum due to this. Human fall detection is one of the most important tasks of medical care system for the aged people. Human fall is a common problem among elderly people. Detection of a fall and providing medical help as early as possible is very important to reduce any further complexity. The chances of death and other medical complications can be reduced by detecting and providing medical help as early as possible after the fall. There are many state-of-the-art fall detection techniques available these days, but the majority of them need very high computing power. In this paper, we proposed a lightweight and fast human fall detection system using pose estimation. We used 'Movenet' for human joins key-points extraction. Our proposed method can work in real-time on any low-computing device with any basic camera. All computation can be processed locally, so there is no problem of privacy of the subject. We used two datasets 'GMDCSA' and 'URFD' for the experiment. We got the sensitivity value of 0.9375 and 0.9167 for the dataset 'GMDCSA' and 'URFD' respectively. The source code and the dataset GMDCSA of our work are available online to access. © 2024, CC BY-NC-ND.

关键词： Fall detection

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Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search

arXiv

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arXiv 2024年

作者： Huang, Tianyu Li, Haoang Peng, Liangzu Liu, Yinlong Liu, Yun-Hui The Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong The Thrust of Robotics and Autonomous Systems and the Thrust of Intelligent Transportation The Hong Kong University of Science and Technology Guangzhou China University of Macau 999078 China

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (> 95 % is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost. © 2024, CC BY.

关键词： Efficiency

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Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes

arXiv

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arXiv 2024年

作者： Meng, Si Yi Orvieto, Antonio Cao, Daniel Yiming De Sa, Christopher Department of Computer Science Cornell University IthacaNY United States ELLIS Institute Tübingen MPI for Intelligent Systems Tübingen AI Center Germany

We study gradient descent (GD) dynamics on logistic regression problems with large, constant step sizes. For linearly-separable data, it is known that GD converges to the minimizer with arbitrarily large step sizes, a property which no longer holds when the problem is not separable. In fact, the behaviour can be much more complex - a sequence of period-doubling bifurcations begins at the critical step size 2/λ, where λ is the largest eigenvalue of the Hessian at the solution. Using a smaller-than-critical step size guarantees convergence if initialized nearby the solution: but does this suffice globally? In one dimension, we show that a step size less than 1/λ suffices for global convergence. However, for all step sizes between 1/λ and the critical step size 2/λ, one can construct a dataset such that GD converges to a stable cycle. In higher dimensions, this is actually possible even for step sizes less than 1/λ. Our results show that although local convergence is guaranteed for all step sizes less than the critical step size, global convergence is not, and GD may instead converge to a cycle depending on the initialization. © 2024, CC BY.

关键词： Logistic regression

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Abdelmalak, Mark Emad Sobhi Khodadadi, Nima Zaki, Ahmed Mohamed Eid, Marwa M. Rizk, Faris H. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Bahrain Polytechnic Faculty of Engineering Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara University Jadara Research Center Irbid Jordan

ISBN: (纸本)9798350351408

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies. © 2024 IEEE.

关键词： Digital storage

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Towards Consistent and Explainable Motion Prediction using Heterogeneous Graph Attention

Towards Consistent and Explainable Motion Prediction using H...

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IEEE Symposium on intelligent Vehicle

作者： Tobias Demmler Andreas Tamke Thao Dang Karsten Haug Lars Mikelsons Robert Bosch GmbH Stuttgart Germany Department of Computer Science and Engineering Institute for Intelligent Systems Esslingen University Germany Leads the Chair of Mechatronics University of Augsburg Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In autonomous driving, accurately interpreting the movements of other road users and leveraging this knowledge to forecast future trajectories is crucial. This is typically achieved through the integration of map data and tracked trajectories of various agents. Numerous methodologies combine this information into a singular embedding for each agent, which is then utilized to predict future behavior. However, these approaches have a notable drawback in that they may lose exact location information during the encoding process. The encoding still includes general map information. However, the generation of valid and consistent trajectories is not guaranteed. This can cause the predicted trajectories to stray from the actual lanes. This paper introduces a new refinement module designed to project the predicted trajectories back onto the actual map, rectifying these discrepancies and leading towards more consistent predictions. This versatile module can be readily incorporated into a wide range of architectures. Additionally, we propose a novel scene encoder that handles all relations between agents and their environment in a single unified heterogeneous graph attention network. By analyzing the attention values on the different edges in this graph, we can gain unique insights into the neural network’s inner workings leading towards a more explainable prediction.

关键词： Measurement Tracking Roads Encoding Trajectory Autonomous vehicles

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A Novel Noise Filtering Method Using Kalman Filter in Digital Holographic Microscopy (DHM)

A Novel Noise Filtering Method Using Kalman Filter in Digita...

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Taishi Ono Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan IITC School of ICT Robotics and Mechanical Engineering Hankyong National University Kyonggi-do Republic of Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Three-dimensional (3D) profile by Digital Holographic Microscopy (DHM) always contains noise. This is an issue in the phase information extraction method of DHM system. However, in the conventional phase information extraction from the frequency domain, it may not extract only phase information. To solve this problem, we propose that the extracting phase information from the frequency domain of recorded hologram using DHM system is specified multiple times and Kalman filtering is performed on the multiple 3D profiles. The purpose of this method is to eliminate randomly generated noise and generate highly accurate 3D profiles. The generated 3D profile is subjectively and quantitatively evaluated with conventional methods. As a result, 3D profile of the proposed method shows the higher accuracy than the conventional method.

关键词： Three-dimensional displays Accuracy Shape Microscopy Frequency-domain analysis Noise Noise reduction

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