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检索条件"任意字段=International Dagstuhl Workshop on Model-Based Engineering of Embedded Real-Time Systems"

共 554 条记录，以下是1-10 订阅

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19th workshop on Dependable Smart embedded and Cyber-Physical systems and systems-of-systems, DECSoS 2024, 11th international workshop on Next Generation of System Assurance Approaches for Critical systems, SASSUR 2024, Towards A Safer systems architecture Through Security, TOASTS 2024 and 7th international workshop on Artificial Intelligence Safety engineering, WAISE 2024 held in conjunction with the 43rd international Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024

19th Workshop on Dependable Smart Embedded and Cyber-Physica...

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19th workshop on Dependable Smart embedded and Cyber-Physical systems and systems-of-systems, DECSoS 2024, 11th international workshop on Next Generation of System Assurance Approaches for Critical systems, SASSUR 2024, Towards A Safer systems architecture Through Security, TOASTS 2024 and 7th international workshop on Artificial Intelligence Safety engineering, WAISE 2024 held in conjunction with the 43rd international Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024

ISBN: (纸本)9783031687372

The proceedings contain 36 papers. The special focus in this conference is on Computer Safety, Reliability, and Security. The topics include: Security and Safety in Urban Environments: Evaluating Threats and Risks of Autonomous Last-Mile Delivery Robots;safe Road-Crossing by Autonomous Wheelchairs: A Novel Dataset and Its Evaluation;automating an Integrated model-Driven Approach to Analysing the Impact of Cyberattacks on Safety;Securing Web Access: PUF-Driven Two-Factor Authentication for Enhanced Protection;enhancing Tunnel Safety with Artery V2X Simulation for real-time Risk Assessment;detecting and Mitigating Errors in Neural Networks;challenges and Limitations of Utilizing Multi-core/Heterogeneous Logic Elements in the Railway Signaling Applications;identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis;Using GPT-4 to Generate Failure Logic;towards an Argument Pattern for the Use of Safety Performance Indicators;enabling Theory-based Continuous Assurance: A Coherent Approach with Semantics and Automated Synthesis;Managing Changing Product Liability Obligations Emerging from New Proposed EU Directive;reaching Consensus on System-of-systems Resilience Assurance: A Case of Mobility as a Service;a Deductive Approach to Safety Assurance: Formalising Safety Contracts with Subjective Logic;a New Approach to Creating Clear Operational Safety Arguments;including Defeaters in Quantitative Confidence Assessments for Assurance Cases;multivariate Security Breach Probability: The Gordon-Loeb model with Copulas;quantum Machine Learning: Perspectives in Cybersecurity;Security modeling Challenges and Research Directions Around the ADVISE Meta Framework;Capability Ambient DO – A Usable Tool for Linux System Security;VUOS: A User-Space Hypervisor based on System Call Hijacking;cyber Risk Propagation on Networks;where Do Safety and Security Mutually Reinforce? A Multi-level model-based Approach for a Consistent Interplay;a Flow-based Credibilit

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embedded Network Risk model based on Random Forest Algorithm 6

Embedded Network Risk Model Based on Random Forest Algorithm

引用

6th international workshop on Advanced Algorithms and Control engineering, IWAACE 2022

作者： Ji, Yuxiang State Grid Jibei Electric Power Company Limited Skills Training Center Baoding Electric Power VOC Baoding071000 China

ISBN: (纸本)9781510657755

By extending and optimizing the COSMIC-FFP model, a measurement method for embedded network systems is proposed, which solves the problem that the COSMIC-FFP model does not support the measurement of real-time systems. At the same time, the network fault analysis and the measurement method based on the network scale are designed to improve the accuracy of the network scale. The experimental results show that through the fault analysis and accurate measurement of the embedded network, the proposed method can find out the weak links of risk management in the process of network engineering, realize the real-time measurement of the network system, and reduce the risk of the network engineering process. © 2022 SPIE.

关键词： Decision trees

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Student Perceptions of AI-Enhanced Adaptive Learning systems: A Pilot Survey 7

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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7th IEEE international Conference and workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Katonane Gyonyoru, Klara Ida Katona, Jozsef Institute of Social Sciences University of Dunaujvaros Department of Organizational Development and Communication Science Dunaujvaros Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback. © 2024 IEEE.

关键词： Contrastive Learning

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ThermWare: Toward Side-channel Defense for Tiny IoT Devices 23

ThermWare: Toward Side-channel Defense for Tiny IoT Devices

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24th international workshop on Mobile Computing systems and Applications, HotMobile 2023

作者： Garg, Nakul Shahid, Irtaza Avllazagaj, Erin Hill, Jennie Han, Jun Roy, Nirupam University of Maryland College Park United States Yonsei University Korea Republic of

ISBN: (纸本)9798400700170

As malware in IoT devices flourishes, defenses are lacking. Traditional antivirus or intrusion detection-based defense techniques fail for the limited computational capabilities and the large diversity of platforms and environments. In this paper, we present ThermWare, a non-intrusive screening method to detect anomalous operations on embedded devices at run-time. ThermWare relies on the observation that electronic circuits generate subtle patterns of heat at the component level when the corresponding module is accessed by the micro-operations (e.g., file-write) of the running code. We propose the use of these side-channel heat signatures captured by a thermal camera to determine the sequence of underlying computations in real time. An early implementation of ThermWare shows success in detecting common malware routines in general-purpose IoT devices. We envision leveraging the thermal side-channel to track the internal operations of an embedded device, which can potentially lead to broader applications in engineering embedded systems, monitoring device health and run-time capacity, assisting embedded coding optimization, and physical layer security analysis. © 2023 Owner/Author.

关键词： embedded systems

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Secured-by-design systems-on-chip : a MBSE approach 34

Secured-by-design systems-on-chip : a MBSE approach

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34th international workshop on Rapid System Prototyping - Shortening the Path from Specification to Prototype

作者： Milan, Raphaele Lagadec, Loic Bossuet, Lilian Bollengier, Theotime Teodorov, Ciprian Univ Jean Monnet UMR 5516 St Etienne France ENSTA Bretagne UMR 6285 Brest France

ISBN: (纸本)9798400704109

Security by Design (SbD) has gained increasing interest over the past decade. While iterative processes and legacy preservation aim to reduce costs and mitigate risks through continuity, SbD encourages a break in the way we do things with a simple idea: dealing with new threats, leading to new risks, requires a complete rethink of our design processes. In embedded systems, security has been more or less left aside for a long time, with performance being the main objective. When security concerns emerged, the response was to adapt existing solutions with security patches. This is neither sustainable (to change from simple embedded systems to complex systems-on-chip) nor simply effective. It is necessary to change the mindset, which will lead to new practices. But the central question is: "How can we put security at the heart of the design process?" The aim of this paper is to contribute to this reflection by providing a rapid prototyping environment (modeling and simulation-based systems engineering) for the hardware mechanisms responsible for the deployment of rights management services.

关键词： System-on-chip Security model-based engineering

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Improved Object Detection Algorithm for workshop Environments based on YOLOv5 9

Improved Object Detection Algorithm for Workshop Environment...

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9th international Conference on Computer and Communications, ICCC 2023

作者： Tan, Jinting Wang, Hongliang Li, Xuerao Zhang, Xiaoxing Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Shenyang Ligong University Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Prov. Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350317251

In the context of workshop-restricted environments, this paper presents an improved lightweight detection algorithm named YOLO-M3C to achieve safety helmet wearing detection on embedded devices in real-time while handling multiple video streams efficiently. YOLO-M3C introduces two key enhancements. Firstly, it replaces the backbone network of YOLOv5 with the more lightweight MobileNetV3 network for feature extraction. This improvement significantly reduces the network's parameter count, resulting in enhanced computational speed and a substantial decrease in computational resource requirements. Secondly, the attention mechanism CBAM is introduced to increase the model's focus on important features, leading to further improvements in detection accuracy. CBAM helps the model better comprehend crucial information in the images, thereby enhancing detection algorithm performance. Experimental results demonstrate that YOLO-M3C achieves increased average precision while shortening the training time by 15%. Additionally, the improved algorithm reduces the parameter count of the YOLOv5 model by 52%, effectively reducing model size and training time, while enhancing detection efficiency. The improved YOLOv5 algorithm can be effectively deployed on embedded devices in workshop environments, enabling efficient real-time processing of multiple video streams and reliable safety helmet wearing detection, thus meeting the real-time detection requirements in workshop settings. © 2023 IEEE.

关键词： Object detection

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Development of the social engineering attack models 1

Development of the social engineering attack models

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1st international workshop on Advanced Applied Information Technologies, AdvAIT 2024

作者： Bokhonko, Oleksandr Lysenko, Sergii Gaj, Piotr Khmelnitsky National University Instytutska street 11 Khmelnitsky29016 Ukraine Silesian University of Technology ul. Akademicka 2A Gliwice44-100 Poland

This study developed specialized models for detecting social engineering attacks, with a focus on spam emails, spear phishing, and trojan emails. Each model captures distinct features of these attacks using machine learning-based detection processes. Utilizing the BotGRABBER framework, which incorporates algorithms such as random forest, decision tree, K-nearest neighbor, and XGBoost, the models analyze characteristics like email metadata, user interaction patterns, attachment behaviors, and network anomalies to differentiate between malicious and legitimate communications. The targeted approach of each model enables tailored detection strategies that address specific social engineering tactics, whether they involve spam, personalized deceptive emails, or malware-infected attachments. For example, the trojan email model concentrates on identifying embedded malware within email attachments, utilizing sandbox environments for controlled testing and analysis. In contrast, the spear phishing model focuses on detecting personalized attack methods by analyzing sender details and links for suspicious patterns. The spam email model, on the other hand, prioritizes content filtering and tracking calls-to-action to distinguish between legitimate emails and mass-distributed spam. Empirical results demonstrate the models’ effectiveness, achieving approximately 99% detection accuracy with a 6% false positive rate. This strong performance highlights the potential of these models to contribute to proactive defense strategies against evolving social engineering threats. By leveraging targeted feature sets and adaptive machine learning algorithms, these models can be effectively deployed in real-world environments to safeguard networks and systems from a wide array of social engineering attacks. © 2024 Copyright for this paper by its authors.

关键词： Phishing

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IoT Measurement System for Monitoring the Energy Exchanges in Renewable Energy Communities

IoT Measurement System for Monitoring the Energy Exchanges i...

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2024 IEEE international workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Liccardo, Annalisa Bonavolonta, Francesco Mottola, Fabio Proto, Daniela University of Naples Federico II Dept. of Electrical Engineering and Information Technology Naples Italy

ISBN: (纸本)9798350385014

The paper deals with an IoT-based monitoring system for the measurement of Energy Exchanges in Renewable Energy Communities. The authors propose a system involving embedded devices, acting as IoT power meters, able to communicate through LoRaWAN technology, according to MQTT protocol. The users belonging to the energy community are able to real time monitor the energy flow;this way they can be continuously aware of possible penalties or they can be informed about the behaviors to adopt to maximize incentives associated with the self-consumption of the produced energy. © 2024 IEEE.

关键词： Internet of things

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Sensors S and P 2023 - Proceedings of the 1st international workshop on Security and Privacy of Sensing systems, Part of: SenSys 2023

Sensors S and P 2023 - Proceedings of the 1st International ...

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1st international workshop on Security and Privacy of Sensing systems, Sensors S and P 2023, Part of: SenSys 2023

ISBN: (纸本)9798400704406

The proceedings contain 6 papers. The topics discussed include: spoofing detection for personal voice assistants;another break in the wall: harnessing fault injection attacks to penetrate software fortresses;URLink: using names as sole internet addresses to tackle scanning attacks in IoT;real-time lightweight cloud-based access control for wearable IoT devices: a zero trust protocol;SensorLoader: bridging the gap in cyber-physical reverse engineering across embedded peripheral devices;and practical software defense for GPS spoofing on a hobby UAV.

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The Good, the Bad, and the Binary: An LSTM-based Method for Section Boundary Detection in Firmware Analysis 18th

The Good, the Bad, and the Binary: An LSTM-Based Method for ...

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18th international workshop on Security (IWSEC)

作者： Remigio, Riccardo Bertani, Alessandro Polino, Mario Carminati, Michele Zanero, Stefano Politecn Milan Milan Italy

ISBN: (纸本)9783031413254;9783031413261

Static analysis tools need information about the ISA and the boundaries of the code and data sections of the binary they analyze. This information is often not readily available in embedded systems firmware, often provided only in a non-standard format or as a raw memory dump. This paper proposes a novel methodology for ISA identification and code and data separation, that extends and improves the state of the art. We identify the main shortcoming of state-of-the-art approaches and add a capability to classify packed binaries' architecture employing an entropy-based method. Then, we implement an LSTM-based model with heuristics to recognize the section boundaries inside a binary, showing that it outperforms state-of-the-art methods. Finally, we evaluate our approach on a dataset of binaries extracted from real-world firmware.

关键词： Binary Analysis Reverse engineering Machine Learning embedded systems

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