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IEEE Vehicular Networking Conference (VNC)

作者： Philipp Meyer Timo Häckel Teresa Lübeck Franz Korf Thomas C. Schmidt Dept. Computer Science Hamburg University of Applied Sciences Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and - as versatile platforms to host Network Anomaly Detection Systems (NADSs) - for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs.

关键词： Systematics Protocols Detectors Real-time systems Vectors Topology Security

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Exploring the Effect of Age and Sex on Subject-Independent EEG-Based Emotion Recognition Methods

Exploring the Effect of Age and Sex on Subject-Independent E...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Camilo E. Valderrama Anshul Sheoran Qian Liu Dept. of Applied Computer Science University of Winnipeg Winnipeg Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

In the field of emotion recognition, there are two approaches used to develop predictive models that recognize emotions from EEG signals: subject-dependent and subject-independent. Subject-independent models, although more practical, tend to yield a lower performance due to the high variability of EEG signals between individuals. Recent studies have indicated that incorporating prior demographic information about individuals can improve the accuracy of subject-independent approaches. Some studies have supported this claim by showing that including individuals’ sex can boost model accuracy. However, until now, no one has used interpretable models to measure to what extent demographics can enhance subject-independent approaches. In this work, we follow this direction by using a logistic regression model to correlate the output of a deep learning model with subjects’ age and sex, thereby evaluating whether these factors impact emotion prediction. Our analysis indicates that the ‘sex’ variable significantly influenced the predictions of the deep learning model in three out of five emotions, whereas ‘age’ does not have any effect. These findings suggest that sex is a factor that needs to be considered when designing EEG-based emotion recognition models, which could lead to more robust subject-independent models with potential applications in areas such as healthcare, education, and marketing.

关键词： Deep learning Emotion recognition Logistic regression Accuracy Stacking Education Medical services

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A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks

arXiv

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arXiv 2024年

作者： Meyer, Philipp Häckel, Timo Lübeck, Teresa Korf, Franz Schmidt, Thomas C. Dept. Computer Science Hamburg University of Applied Sciences Germany

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and – as versatile platforms to host Network Anomaly Detection Systems (NADSs) – for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Zoom-DF: A Dataset for Video Conferencing Deepfake 1

Zoom-DF: A Dataset for Video Conferencing Deepfake

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1st ACM Workshop on Security Implications of Deepfakes and Cheapfakes, WDC 2022, co-located with ACM AsiaCCS 2022

作者： Park, Geon-Woo Park, Eun-Ju Woo, Simon S. Computer Science & Engineering Dept. Sunkyunkwan University Suwon Korea Republic of Applied Data Science Dept. Sunkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781450391788

With the rapid growth of deep learning methods, AI technologies for generating deepfake videos also have been significantly advanced. Nowadays, the manipulated videos such as deepfakes are so sophisticated that one cannot easily differentiate between real and fake, and one can create such videos with little effort. However, such technologies can be likely to be abused by people with malicious intents. To address this issue, approaches and efforts to detect deepfakes have been researched significantly. However, the performances of the detectors in general depends on the quantity and quality of the training data. In this paper, we introduce a new deepfake dataset, Zoom-DF, which can be injected during the remote meeting and video conferencing, to create a sequence of fake participant images. While most deepfake datasets focus on the face area, our dataset primarily targets for the remote meeting, and manipulates movements of the participants. We evaluate existing deepfake detectors on our new Zoom-DF dataset and present the performance results. © 2022 ACM.

关键词： Video conferencing

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Preparing for Super-Reactivity: Early Fault-Detection in the Development of Exceedingly Complex Reactive Systems

arXiv

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arXiv 2024年

作者： Harel, David Marron, Assaf Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving, and heavily interwoven with other systems and the physical world. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. However, the complexity of the system and its interactions and the absence of adequate technical details pose a great obstacle. We propose an architecture for models and tools to overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The approach is facilitated by the inference and abstraction capabilities and the power and knowledge afforded by large language models and associated AI tools. It is based on: (i) deferred, just-in-time interpretation of model elements that are stored in natural language form, and (ii) early capture of tacit interdependencies among seemingly orthogonal requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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Detecting and Mitigating Adversarial Perturbations to Improve E-Commerce Security 11

Detecting and Mitigating Adversarial Perturbations to Improv...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Tariq, Usman Jaafar, Fehmi Malik, Yasir Prince Sattam Bin Abdulaziz University College of Business Administration Dept. of Management Information Systems Saudi Arabia Université du Québec à Chicoutimi Dept. d'Informatique et de mathématique Canada Bishop's University Dept. of Computer Science Canada

ISBN: (纸本)9798350367300

E-commerce platforms face the critical challenge of adversary events, including fraudulent transactions and fake reviews, which can lead to significant financial and reputational damage. Addressing this, our research introduces a hybrid Deep Learning model, tailored for the detection of such adversarial activities. This innovative approach leverages spatial and sequential data processing capabilities, enhancing the identification of subtle adversarial manipulations across diverse e-commerce contexts. Our findings indicate a high detection rate of 93 percent for adversarial attacks, with precision, recall, and Matthews Correlation Coefficient metrics underscoring the model's efficacy. This work significantly contributes to e-commerce security by advancing the robustness of detection systems against a spectrum of adversarial threats, including account takeovers and deceptive reviews. While demonstrating a notable improvement over existing methods, our research also acknowledges the potential for evasion by sophisticated attacks, highlighting areas for future work in enhancing model resilience. This balance of innovation and critical insight provides a solid foundation for further advancements in the field of e-commerce security © 2024 IEEE.

关键词： Adversarial machine learning

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Lightweight CNN based on Spatial Features for a Vehicular Damage Detection System

Lightweight CNN based on Spatial Features for a Vehicular Da...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Polap, Dawid Jaszcz, Antoni Prokop, Katarzyna Srivastava, Gautam Silesian University of Technology Faculty of Applied Mathematics Kaszubska 23 Gliwice44-100 Poland Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada

ISBN: (纸本)9798350324457

Autonomous vehicles are a key element of the automotive industry, where the impact of the human factor on the condition of the vehicle and driving is minimized. An important element is the analysis of vehicular condition, which allows maintainence of its value and correct operation. We propose a system based on the analysis of the image of vehicles, which determines whether there is any damage. For this purpose, we propose a new model of a Convolutional Neural Network (CNN) that has 0. 395M trained values. The architecture of the network is adapted to the analysis of spatial features that allow networks to be adapted to analyze primarily vehicular shape and orientation in relation to other objects. The model also implements spatial dropout and regularization techniques for preventing overtraining and maintaining model generalization. The modeled architecture contributes to obtaining high classification accuracy at 94.78% using a public database and exceeding metrics of known transfer learning models. © 2023 IEEE.

关键词： convolutional neural networks image processing spatial attention spatial features vehicle damage problems

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AGV Quality of Service Throughput Prediction via Neural Networks

AGV Quality of Service Throughput Prediction via Neural Netw...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Silesian University of Technology Faculty of Applied Mathematics Kaszubska 23 Gliwice44-100 Poland Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada

ISBN: (纸本)9798350324457

The recent development of Autonomous Guided Vehicles (AGV) use in industry has resulted in the need to model new solutions based on the latest technological achievements. One of the areas worth attention and development is Quality of Service (QoS) in relation to communication between vehicles. QoS makes it possible to divide the bandwidth in such a way that tasks performed by devices are completed with a certain priority. However, in order to manage these resources effectively, it is necessary to anticipate available network throughput. Therefore, this paper presents a neural-based model to ensure throughput prediction for AGV. The proposed solution assumes the use of information on both historical throughput values and data obtained from other sensors that AGV are equipped with. Therefore, the idea is to integrate two neural networks with another network, which is supposed to predict the result based on these two previously obtained predictions. Ultimately, prediction results with a Root Mean Squared Error (RMSE) of 0.1 for the downlink and 1.6 for the uplink were obtained. © 2023 IEEE.

关键词： AGV deep learning predictive models QoS throughput prediction

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Modification of Characteristics of Bragg Structure Based on Chebyshev Approach 28

Modification of Characteristics of Bragg Structure Based on ...

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28th IEEE International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED 2023

作者： Andreev, M.V. Drobakhin, O.O. Computer Science and Information Technology Dept. Oles Honchar Dnipro National University Dnipro Ukraine Applied Radiophysics Electronics and Nanomaterials Dept. Oles Honchar Dnipro National University Dnipro Ukraine

ISBN: (纸本)9798350315332

Bragg-type layered dielectric structure, which provides the Chebyshev character of the behavior of the reflection frequency response, was considered. To obtain a fractional-rational representation of the structure frequency response, method of synthesis of the type I Chebyshev filter was used. An approach for mapping the entire equiripple domain of Chebyshev polynomial onto the specified reflectionless band and determining scale parameter are proposed. A modification of the periodic Bragg-type layered structure is demonstrated, which provides a reflection frequency response with minimum ripple level in the passband while preserving the location of the zeros that delimit the reflection band of the structure. © 2023 IEEE.

关键词： Chebyshev polynomials

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Enhancing Printed Circuit Board Defect Detection through Ensemble Learning 1

Enhancing Printed Circuit Board Defect Detection through Ens...

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1st IEEE International Workshop on Future Intelligent Technologies for Young Researchers, FITYR 2024

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Gao, Jerry Liu, Jun San José State University Department of Applied Data Science San José United States Virginia Tech Dept. of ISE Blacksburg United States San José State University Dept. of Computer Engineering San José United States

ISBN: (纸本)9798331539757

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-The-Art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024 IEEE.

关键词： Contrastive Learning

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