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Pro-CHASER: A Prototype testbed for Car Hacking Attack Simulation and Evaluation using Raspberry Pi to Investigate Threat Landscape

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IEEE Access 2025年 13卷 36401-36420页

作者： Vanajakshi, J. Renuka, A. Kamath, Vidya Adesh, N.D. Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Karnataka Manipal576104 India

Recent advancements in transportation technology have necessitated a seamless integration of sensors, cameras, and communication protocols for autonomous vehicles. Ensuring cybersecurity for these vehicles has become critical, as they are vulnerable to attacks that could jeopardize safety and performance. While machine learning and deep learning models could be developed to address cybersecurity issues in autonomous vehicles, developing these models requires large datasets for training. Obtaining real-world data from actual vehicles also presents significant challenges. To address these limitations, a low-cost Pro-CHASER testbed - Prototype testbed for Car Hacking Attack Simulation and Evaluation using Raspberry Pi - was developed using a Controller Area Network (CAN) bus network with CAN-HAT (Hardware Attached on Top). This testbed utilized CAN to simulate autonomous vehicle functions and explore attack vectors. Microsoft Threat Modelling Tool, and MITRE ATT&CK framework were used to identify the vulnerabilities in the context of autonomous vehicles. The identified attacks were exploited using the designed testbed by employing many security testing tools to simulate CAN message injection attacks, USB exploits, and ROS-based fake image publisher attack, respectively. Through these attack simulations, the testbed functionalities were successfully compromised. The results demonstrated that message injection into the CAN bus manipulated sensor data that led to erroneous decision-making. Unauthorized access to the physical access points led to the injection of a malicious payload via a USB port. The computer vision system's response to the false data ultimately compromised the vehicle's perception and control. The findings highlight the capability of Pro-CHASER in identifying cyber security risks in autonomous vehicles, which offers a practical and low-cost platform for threat simulation and evaluation. © 2013 IEEE.

关键词： Testbeds

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ImmuneChain: A Blockchain-Based Secure and Transparent Vaccine Supply Chain

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SN computer science 2025年第1期6卷 1-13页

作者： Arsheen, Sobiya Ahmad, Khaleel Department of Computer Science and Information Technology Maulana Azad National Urdu University (A Central University) Hyderabad India

Blockchain technology has the capability to handle information in a decentralized, permanent, transparent, and secure manner, which makes it unique. The data of the vaccine supply chain is maintained either manually or kept on a central server, which can be removed or tampered with. The integration of blockchain technology into the vaccine supply chain gives users a means of establishing trust. Utilizing blockchain technology to counter challenges in the global supply chain management of medicinal products is expanding quickly. Blockchain leads to secured, transparent, and traceable data over the network. This research proposes a blockchain-based vaccine supply chain to guarantee the efficiency of vaccine distribution in health centers, as well as the traceability, accuracy, and trustworthiness of immutable records. A blockchain-based supply chain acts as a safe, efficient, and transparent link in the distribution of vaccines. The proposed work connects all the stakeholders, such as the manufacturer, distributor, and health centers, using the Hyperledger Fabric blockchain platform. It also provides an interface for users to verify the authenticity of the vaccine vial and its quality. The performance of the proposed system is tested by the Hyperledger Caliper tool. The blockchain-based vaccine supply chain improves data availability and efficiency since all network participants get the responses instantly. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Hyperledger fabric Supply chain Traceability Vaccines

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The Design of Portrait Photography Guiding system Using VGG16 Model 14th

The Design of Portrait Photography Guiding System Using VGG1...

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14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Chuan-Feng Huang, Chun-Hong Tseng, Hsiang-Chieh Wu, Pei-Jung Lin, Wei-Sheng Peng, Hsiang-Min Department of Information Management Minghsin University of Science and Technology Hsinchu Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

Because of the population of photography camera, human being could have photography equipment to take a picture became an easy task. However, to have a good photography is not an easy task. The basic of a good photo is the composition of the photography. A better photography composition could lead better visual effect. So, in this paper we intent to design a photography guiding system framework for users to have good photos especially for portrait photography. In our work, we design a system framework using VGG16 model based on Rule of Thirds and luminance distribution to guide user to select better photography composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photographic equipment

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IoT-Supported Indoor Navigation system Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation system (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning system (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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An Intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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Decentralizing Social Data: A Blockchain-Based Approach to Transparent and Trustworthy Social Networks 5

Decentralizing Social Data: A Blockchain-Based Approach to T...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： El Ghor, Hussein Nakhal, Bilal Nasr, Sara Daher, Mohamad MUBS University ARCAS Research Center Faculty of Arts and Sciences Beirut Lebanon Beirut Arab University Mathematics and Computer Science Department Beirut Lebanon Lebanese University Faculty of Science Department of Computer Science Beirut Lebanon AUT University Department of Computer Science and Information Technology Beirut Lebanon

ISBN: (纸本)9798331519957

The rise of online social network (OSN) platforms has revealed pivotal issues with regards to data transparency and the concerns on the user's consent. Users are barely aware of how their data is used. To this end, this paper investigates the notion of applying blockchain technology for decentralizing social data, thus bringing more privacy and trust to the abstract concept of OSN platforms. We propose a Blockchain-Based Distributed Social Network system (B2DSN) that uses Hyperledger Fabric and InterPlanetary File system (IPFS) to secure sovereignty rights of data and offer greater control to users' data subjects. Hyperledger fabric is used to collect friends and data that are analyzed from social networks. We integrate consensus and verification methods directly within topic-based communities to provide trustworthiness of OSN platforms and eliminate the dissemination of rumors, and fake news. The feasibility of B2DSN is proved based on the integration of decentralized overlay network data flow, data sovereignty and verification and topic-based communities. The experimental simulation for data integrity demonstrates that the B2DSN model based on blockchain is feasible in securing and verifying the authenticity of social data even for large-scale OSNs. © 2025 IEEE.

关键词： Blockchain hyperledger fabric IPFS privacy Social network transparency

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Bitcoin Price Prediction Using Deep Learning Models 4

Bitcoin Price Prediction Using Deep Learning Models

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Hajare, Rasika Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research. © 2025 IEEE.

关键词： Prediction models

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Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) Classification and Prediction of Heart Disease 4

Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chairmadurai, P. Kavitha, P. Kamalakkannan, S. Pallavaram Department of Computer Science Chennai India Pallavaram Department of Computer Applications Chennai India Pallavaram Department of Information Technology Chennai India

ISBN: (纸本)9798331523923

Medical data analysis is increasingly used to plan, improve research techniques, and explain diagnoses. The allocation of medical resources is based on the prevalence of specific pathologies. Artificial intelligence (AI) offers robust software solutions to analyze existing data and provide optimal predictions of medical outcomes. The dataset for heart disease prediction consists missing values in important features like cholesterol, ECG results and lifestyle factors. This research focuses to solve the issues in the traditional machine learning algorithm and designing a system model incorporating multiple data processing algorithms to classify heart disease. The classification process allows for the generation of a predictive model from training and testing datasets. These datasets are processed using an algorithm for classification to create a new model that can analyze detailed data, potentially with these same classes, by combining computational techniques with mathematical tools. This study uses optimization strategies to improve this model's performance and maximize prediction outcomes. This study aims to provide a framework to forecast cardiac problems based on significant risk using various classifier techniques, including the Bayesian Optimized SVM, to variables. The UCI repository is the source of the dataset utilized in this investigation. Linear Discriminant Analysis is used for feature extraction to decrease dimensionality and enhance the model's capacity to identify relevant patterns in the data. To generate the results as a precision, Recall, f1 score and accuracy. © 2025 IEEE.

关键词： Support vector machines

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced computer science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (science and information Organization). All rights reserved.

关键词： Rain

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BeGrading: large language models for enhanced feedback in programming education

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Neural Computing and Applications 2025年第2期37卷 1027-1040页

作者： Yousef, Mina Mohamed, Kareem Medhat, Walaa Mohamed, Ensaf Hussein Khoriba, Ghada Arafa, Tamer Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt

In recent years, large language models (LLMs) have gained significant traction across various domains, including education. This paper explores the application of LLMs in grading programming assignments. By leveraging data collected from existing programming assignments and their corresponding grades, we aim to develop a robust LLM-based grading system. We also incorporate augmented data representing various grading scenarios to enhance the model’s performance and ensure comprehensive coverage across all grading levels. Our approach involves training the LLM on this combined dataset to enable accurate and consistent evaluation of programming assignments. The proposed model, BeGrading, aims to reduce the grading burden on educators and provide timely and objective feedback to students. Compared to the Codestral model, our proposed model demonstrates an absolute difference rate of 19%, equivalent to ±0.95 out of 5. This is acceptable for using a small, fine-tuned model with optimized data. Additionally, the Codestral model compared to the dataset optimized score shows a difference of 15% equivalent to a margin of ±0.75 out of 5. Preliminary results demonstrate the potential of LLMs to perform grading tasks with a high degree of reliability, opening avenues for further research and practical applications in automated education systems. © The Author(s) 2024.

关键词： Digital elevation model

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