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Adaptive and Lightweight Cyber-Attack Detection in Modern Automotive Cyber-Physical Systems

Adaptive and Lightweight Cyber-Attack Detection in Modern Au...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Youngmi Baek Seo-Hee Park Dept. of Computer Software Engineering Changshin University Changewon Korea

Modern automotive cyber-physical systems (CPS) may no longer refer to the means of traditional automobiles over time. As the automotive CPS are connected to everything in the real world, they will be transformed into a personalized entertainment space for a more comfortable and enjoyable trip. The fundamental for serving all these changes should be that automotive CPS are safely controlled during traveling. In the automotive CPS, a controller area network is well known as a reliable communication protocol for real-time control applications. However, due to the lack of vehicular security functionality, they expose critical vulnerabilities to cyber-attacks, which are injected from external links, targeting the modern CPS. In this paper, to detect these cyber-attacks, we develop a noble message authentication mechanism to be implemented in the existing electronic control units. It does not require any hardware security module to increase production costs. Our preliminary result demonstrates that the proposed mechanism can protect the vehicular system against fuzzy attacks.

关键词： Adaptive systems Protocols Production Cyber-physical systems Real-time systems Space exploration Reliability

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Synthetic Data Generation: An Evaluation of the Saving Images Pipeline in Unity

Synthetic Data Generation: An Evaluation of the Saving Image...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Sankar, Kevin Pooransingh, Akash Ramroop, Sarika The University of the West Indies Dept. of Electrical and Computer Eng. St Augustine Trinidad and Tobago Software Engineering Virtana Tt Ltd. St Augustine Trinidad and Tobago

ISBN: (纸本)9798350327595

In this paper, we propose a new framework, CUDA DirectX 11 Interop, for the Saving Images Pipeline that can exceed current performance standards. Unity generates synthetic data to supplement the large amounts of training data required by neural networks. The Saving Images Pipeline saves synthetic data to disk, but is not optimized due to overhead costs. Current (Linear and Coroutines) and alternative (DirectX 11 and CUDA OpenGL Interop) solutions were evaluated in terms of execution times, image resolutions, and JPEG qualities. The DirectX 11 solution exhibited the best performance as it met the 40 ms baseline. Using CUDA Interoperability with the DirectX 11 solution creates a new framework, CUDA DirectX 11 Interop, that exceeds the performance of all solutions examined. © 2023 IEEE.

关键词： computer Vision CUDA DirectX Image Generation JPEG Compression Unity

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An Efficient Detection Model for Brain Stroke Based on Transfer Learning

An Efficient Detection Model for Brain Stroke Based on Trans...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Faroq H. M. Mohammed Abdulfattah Ba Alawi Ferhat Bozkurt M. Dilruba Geyikoğlu Electrical and Electronic Engineering Dept. Faculty of Engineering Ataturk University Erzurum Turkey Computer Engineering Dept. Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Dept. Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Brain stroke is one of the most common causes of death, ranking as the second leading cause worldwide. With the advancements of deep learning, the detection of brain strokes from CT images becomes possible. In this study, we propose a method for classifying brain stroke images and predicting the presence of a stroke using convolutional neural networks (CNNs), which are particularly effective in image classification tasks. These techniques involve training different CNN models (EfficientNetB0-B7, InceptionResNetV2, ResNet50, ResNet152, and VGG19) on a dataset of labeled brain strokes and normal brain images. Through this process, the model learns to identify patterns and features that are indicative of a stroke. The results show that the different CNN models were able to classify stroke images with high accuracy and predict the presence of stroke with an overall accuracy of 99.95%, which is a significant improvement compared to similar studies. This demonstrates the potential of deep learning-based approaches using CNNs in assisting the diagnosis of strokes and aiding in early-stage treatment. Finally, we provide an analysis of model performance, highlighting each CNN model used, and providing insights for future research.

关键词： Training Analytical models Accuracy Transfer learning Stroke (medical condition) Brain modeling Convolutional neural networks

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Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction 4

Reinforcement Learning for Language Grounding: Mapping Words...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adapa, Srinivasa Rao Gupta, Himanshu Mukherjee, Susmoy Mallupalli, Praneeth Reddy Avhad, Priyanka Upreti, Kamal OpenLogix LLC Software Engineer MI United States Software Engineer Meta NJ United States VIT Institute Electrical and Electronics Engg. Tamil Nadu India Arcesium Hyderabad India Veermata Jijabai Technological Inst. Computer Engineering Mumbai India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. © 2024 IEEE.

关键词： Reinforcement learning

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Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System 9

Enhancing Women's Safety Through Customized Smartphone Senso...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

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A Proposed Framework for Smart Geo-Fencing for Religious Places 7

A Proposed Framework for Smart Geo-Fencing for Religious Pla...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Vishnoi, Ankit Choudhury, Tanupriya Patni, Jagdish Chandra Sar, Ayan Mahdi, Hussain Falih Maharashtra Pune412115 India CSE Dept. Lavale Campus Maharashtra Pune412115 India Pune India School of Computer Science Uttarakhand Dehradun248007 India University of Diyala Dept. of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350383065

The innovative system known as 'Smart Geo-fencing for Religious Places' integrates Blockchain and Zero Power Device technology to enhance the reverence of sacred sites. By amalgamating the robustness of blockchain with geofencing capabilities and electronic apparatuses, this solution delineates invisible perimeters encircling religious structures like mosques, churches, and temples. As worshippers enter these digitally marked zones with their smartphones, the setup is capable of monitoring select operations such as making calls, sending texts, or receiving app alerts to foster a serene atmosphere. Furthermore, leveraging blockchain's prowess guarantees that all recorded data remains secure and unalterable. The incorporation of devices that consume minimal energy is crucial for ensuring sustainability. This sophisticated fusion promotes tranquility within religious communities by adeptly marrying contemporary technological advancements with age-old spiritual practices. © 2023 IEEE.

关键词： Blockchain

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Explainable Email Spam Detection: A Transformer-based Language Modeling Approach

Explainable Email Spam Detection: A Transformer-based Langua...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Amaz Uddin Md. Mahiuddin Al Akram Chowdhury Dept. of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Dept. of Software Engineering Metropolitan University Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Email spam, also known as junk email, is any unwanted or irrelevant message that is delivered to a large number of recipients without the recipient's consent. Email communication is undoubtedly efficient and convenient, but there are serious risks associated with its rise in unwanted spam emails. These risks include resource waste and potential cybersecurity breaches. It also serves as a backdoor for malware infections, phishing attempts, and other cyber threats. Transformer-based models have a positive effect on the cybersecurity sector in order to address those cybersecurity concerns. In this research, we use a Bidirectional Encoder Representations from Transformers (BERT) variant, the DistilBERT model, to detect spam emails. For this purpose, we utilize the benchmark Enron spam email dataset, applying several preprocessing techniques to address the noisy data. Our model demonstrates high accuracy, achieving 99.18%. Additionally, to interpret the model's decision-making process, we used Transformers Interpret. This explainability tool provides insights into how our proposed model works and makes its decisions throughout the experiment.

关键词： Unsolicited e-mail Large language models Phishing Decision making Benchmark testing Transformers Robustness Malware Noise measurement Information technology

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Federated Learning with Clustering-Based Participant Selection for IoT Applications

Federated Learning with Clustering-Based Participant Selecti...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Wang, Kevin I-Kai Ye, Xiaozhou Sakurai, Kouichi The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Dept. of Informatics Fukuoka Japan

ISBN: (纸本)9781665480451

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance. © 2022 IEEE.

关键词： Internet of things

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Self-Optimizing Memory Controllers: Proposing Request-level Scheduling 2

Self-Optimizing Memory Controllers: Proposing Request-level ...

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2nd International Conference on computer Science, engineering and Applications, ICCSEA 2022

作者： Shawky, Alaa Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Faculty of Engineering Electronics and Communications Dept. Cairo Egypt Siemens Digital Industries Software Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Dept. Cairo Egypt

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Self-Optimizing Memory Controllers present a great potential in the future of memory controllers. As they alleviate the burden of designing an optimal memory scheduling policy, while providing adaptability to different workloads and systems. Previous state-of-art controllers scheduled memory accesses on the command level. This causes the agent to lose sight of the relation between commands and can lead to the issuing of unuseful sequences of precharge/activate of rows or closing banks that should have been left open, and vice versa. In this work we propose a bank-aware self-optimizing memory controller that schedules memory references on the request-level, presenting to the agent the actual problem it needs to tackle. This simplifies the evaluation of system attributes and eliminates the need for correction mechanisms that was needed in previous controllers. With the proposed algorithm a maximum of 18% increase in the data bus utilization is achieved compared to the commonly used FR-FCFS algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Faridoon, Fatima Ali, Raja Hashim Ul Abideen, Zain Shahzadi, Nazia Ijaz, Ali Zeeshan Arshad, Usama Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis. © 2023 IEEE.

关键词： Feature Selection

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