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

共 42758 条记录，以下是41-50 订阅

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Hunting Vulnerabilities in the Maritime Domain: A Domain Wide Cybersecurity Vulnerability Analysis 19th

Hunting Vulnerabilities in the Maritime Domain: A Domain ...

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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： Zafar, Abdullah Amro, Ahmed Norwegian University of Science and Technology NTNU GjØvik Norway

ISBN: (纸本)9783031823480

the maritime domain, constituting over 80% of global trade, is a critical component of the world economy, facilitating the transfer of vast quantities of goods across the globe. In this way, the success of other crucial sectors vital to the normal functioning of life relies heavily on the pivotal role of the maritime domain. People have been navigating waterways through ships since ancient times. With technological progress, the structure and functioning of these ships have changed drastically. Modern ships comprise a wide array of interconnected and complicated information and operational cyber physical systems. With increasing connectivity through modern communication technologies, these systems are fast becoming connected to the outside world. Furthermore, the trend of remote-controlled and autonomous ships is expected to increase soon. Hence, assessing the cybersecurity posture of the maritime systems and identifying & fixing the vulnerabilities that comprise those ships is of utmost importance. To achieve these objectives, this research contributes in two ways, 1) proposing a comprehensive vulnerability and threat analysis methodology, named MathreX developed to compile maritime-related known vulnerabilities and gather related threat-related information providing key insights to enhance decision-making and security measures, and 2) results from utilizing MathreX for conducting a domain-wide analysis of the vulnerabilities found in the maritime assets and infer threat-related information to capture the threat landscape in the domain. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cybersecurity

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Enabling Android Application Monitoring by Characterizing Security-Critical Code Fragments 19th

Enabling Android Application Monitoring by Characterizing S...

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作者： Eikerling, Hendrik Kampkötter, Anemone Safe and Secure IoT Systems Fraunhofer IEM Paderborn Germany Department of Computer Science Technische Universität Dortmund Dortmund Germany

ISBN: (纸本)9783031823619

Resource-constrained devices, such as Android smartphones, cannot currently benefit from protection through application monitoring, since usual approaches are too resource intensive. To develop an efficient and effective solution suitable for mobile devices, we propose to monitor only those aspects of application execution that are potentially security-critical. the aim of this work is to support developers in instrumenting security-critical code fragments in their applications, so that they can be monitored dynamically. In this paper, we present our approach to characterizing and dynamically instrumenting these fragments in Android application code. As a proof of concept, we show the efficacy of machine learning-based malware detection using the identified fragments as input. Our evaluation indicates the ability to detect various kinds of threats in instrumentation-based attack detection, enabled by automated dynamic analysis, and focuses in particular on the savings potential offered by our filtering approach. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Android malware

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Algorithms for the Collaborative Delivery Problem with Monitored Constraints 19th

Algorithms for the Collaborative Delivery Problem with Mo...

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19th international conference and Workshops on Algorithms and Computation, WALCOM 2025

作者： Blank, Lotte Huynh, Kien C. Luo, Kelin Naredla, Anurag Murty Institute of Computer Science University of Bonn Bonn Germany Communications and Transport Systems ITN Linköping University Linköping Sweden Department of Computer Science and Engineering University at Buffalo GetzvilleNY United States

ISBN: (纸本)9789819628445

We provide efficient algorithms to solve package delivery problems in which a sequence of drones work together to ‘optimally’ deliver a package from a source s to a target t. the package may be transferred from one drone to another only on a given line on which secure package transfers are ensured (we refer to this as monitored constraints). We allow the source and target to lie outside of this line. the drones have different starting locations, speeds, and rates of fuel consumption. Two notions of optimality are studied—fuel-efficient delivery (minimize total fuel consumption in a successful delivery), and fastest delivery (minimize total time for delivery). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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Proposition of a Novel Type of Attacks Targetting Explainable AI Algorithms in Cybersecurity 19th

Proposition of a Novel Type of Attacks Targetting Explain...

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作者： Szelest, Sebastian Pawlicki, Marek Pawlicka, Aleksandra Kozik, Rafal Choraś, Michal ITTI Sp. z o.o. Poznań Poland Bydgoszcz University of Science and Technology Bydgoszcz Poland University of Warsaw Warsaw Poland

ISBN: (纸本)9783031823619

Artificial intelligence systems, particularly those utilizing deep neural networks, are increasingly integrated into critical sectors like healthcare, finance, and security. these systems’ reliance underscores the importance of explainable AI, commonly known as xAI, which aims to explain the decision-making processes of AI models. However, xAI’s integrity is challenged by adversarial attacks that manipulate explanations without affecting the model’s output. this paper introduces a novel method to generate such attacks, highlighting their potential to mislead operators and compromise system trustworthiness. By detailing the susceptibility of xAI to these explanation attacks, the study emphasizes the need for robustness in xAI methods. It proposes that ensuring the fidelity of explanations under adversarial conditions is crucial for maintaining the transparency and reliability of AI systems. through this work, the authors aim to advance understanding of security vulnerabilities in xAI and contribute to the development of AI systems that are both transparent and resilient against adversarial threats. this investigation into the phenomenon of explanation attacks opens a new possibility for enhancing the security protocols surrounding AI systems in sensitive applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Contemporary Impact of Cyberbullying on Youngsters: An Appraisal of Psychological, Emotional, Physical Health, and Academic Implications 19

Contemporary Impact of Cyberbullying on Youngsters: An Appra...

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

作者： Faith, Fatokun B. Long, Zalizah Awang Hamid, Suraya Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science IT Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Cyberbullying is now a serious issue in contemporary times. the impact of cyberbullying on youngsters is of enormous concern in the public health sector as well as in the cybersecurity field. Several researchers have discussed the impact of cyberbullying from diverse perspectives, however, there is still a large gap in finding the paramount impact of cyberbullying in contemporary times, as well as providing solutions to eradicate this menace, especially as it deals with the negative effects of technology which is emerging daily. this study reviews via a scoping evidence approach suggesting the impact of cyberbullying on youngsters, focusing on the psychological, emotional, physical health, and academic implications. Findings from studies revealed that most youngsters face more of the psychological and emotional consequences of cyberbullying as compared to others discussed in the literature. Interestingly, there were also revelations of how cyberbullying had negative influences on the physical health and academic achievements of youngsters. these findings thus instigate the need for novel approaches that can help curb the rampancy of cyberbullying as well as ensure that decision-makers and stakeholders in the respective institutions put these impacts of cyberbullying into consideration when designing cybersecurity policies to ensure that victims of cyberbullying are assisted to recover, put in place technological solutions that can track and punish offenders and also reduce the rate of cyberbullying victimization. © 2025 IEEE.

关键词： Electronic health record

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

this study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. the research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. the model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. the attack type of each timestamp were visualized using distinct colors to the grid images. these images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. this graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. this research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

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

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. the results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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computer Vision Based Quality control for Molding Injection Machines 19th

Computer Vision Based Quality Control for Molding Injection...

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19th international conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Moreno, Ramón García, Oscar Del Río Cristobal, Miguel Shankar Muthuselvam, Revanth María Sanjuan, José Vallejo, Andrés Wang, Ting Department of Advanced Manufacturing 4.0 Antolin Engineering Burgos Spain Instituto Tecnológico Catilla y Leon Burgos Spain College of Electrical Engineering and Control Science Nanjing Tech University and Southeast University Nanjing China

ISBN: (纸本)9783031750090

this paper presents a comprehensive study on the application of computer vision, specifically Convolutional Neural Networks (CNNs), for quality control in automotive interior manufacturing using Injection Molding Machines (IMMs). Despite the high precision and productivity of IMMs, a small percentage of defects persist. Addressing this, the paper compares four popular CNN algorithms—ResNet-50, SegNet, FCN, and PSPNet—on their ability to detect and analyze defects in post-manufacturing. the study outlines the advantages of IMMs, the challenges in defect management, and the potential of CNNs to revolutionize quality control processes. Experimental results demonstrate the efficacy of these algorithms, paving the way for enhanced quality management and reduced defect rates in the automotive sector. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Assuring Privacy of AI-Powered Community Driven Android Code Vulnerability Detection 19th

Assuring Privacy of AI-Powered Community Driven Android Cod...

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作者： Senanayake, Janaka Kalutarage, Harsha Piras, Luca Al-Kadri, Mhd Omar Petrovski, Andrei School of Computing Robert Gordon University AberdeenAB10 7QB United Kingdom Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9783031823619

the challenge of training AI models is heightened by the limited availability of data, particularly when public datasets are insufficient. While obtaining data from private sources may seem like a viable solution, privacy concerns often prevent data sharing. therefore, it is essential to establish a system that effectively balances privacy concerns with the need for data. In our previous work, we introduced "Defendroid", which focuses on real-time Android code vulnerability detection using a blockchain federated neural network with explainable artificial intelligence. In this study, the Defendroid approach is enhanced by incorporating variable differential privacy techniques to ensure the privacy of the model training process. the proposed method significantly improves privacy, achieving a privacy budget between 1 and 1.5, while maintaining Defendroid’s baseline accuracy of 96% and an F1-Score of 0.96. As a result, this research thoroughly addresses concerns about the privacy of source code, filling a critical gap. this advancement not only showcases the effectiveness of the new approach but also its capability to address the significant challenges of privacy and data scarcity in AI-driven, community-focused Android code vulnerability detection. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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19th international conference and Workshops on Algorithms and Computation, WALCOM 2025

19th International Conference and Workshops on Algorithms an...

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19th international conference and Workshops on Algorithms and Computation, WALCOM 2025

ISBN: (纸本)9789819628445

the proceedings contain 26 papers. the special focus in this conference is on Algorithms and Computation. the topics include: Computing Conforming Partitions with Low Stabbing Number for Rectilinear Polygons;on the Approximability of Graph Visibility Problems;algorithms for the Collaborative Delivery Problem with Monitored Constraints;a Piecewise Approach for the Analysis of Exact Algorithms;parameterized Complexity of (d, r)-Domination via Modular Decomposition;multipacking and Broadcast Domination on Cactus Graphs and Its Impact on Hyperbolic Graphs;evaluating Monotone Circuits on Surfaces;ranking and Unranking of the Planar Embeddings of a Planar Graph;optimal Uniform Shortest Path Sampling;an Efficient Implementation of Cosine Distance on Minimal Absent Word Sets Using Suffix Automata;popularity on the 3D-Euclidean Stable Roommates;independent Set Reconfiguration Under Bounded-Hop Token Jumping;approximation Algorithms for Non-sequential Star Packing Problems;reconfiguration Using Generalized Token Jumping;parameterized Algorithms for the Spanning Forest Isomorphism (or Containment) on Tree Problems;min-Sum Disjoint Paths on Subclasses of Chordal Graphs;maximize the Rightmost Digit:Gray Codes for Restricted Growth Strings;NP-Completeness and Physical Zero-Knowledge Proofs for Zeiger;abelian and Stochastic Sandpile Models on Complete Bipartite Graphs;changing Induced Subgraph Isomorphisms Under Extended Reconfiguration Rules;a Unified Model of Congestion Games with Priorities: Two-Sided Markets with Ties, Finite and Non-affine Delay Functions, and Pure Nash Equilibria;Dag-Like Unit Refutations in UTVPI Constraint systems;online Contention Resolution Schemes for Size-Stochastic Knapsacks;the Connected k-Vertex One-Center Problem on Graphs.

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