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International Conference on Electrical, Communication, and computer engineering (ICECCE)

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

Modern vehicles are equipped with a large number of electronic control units (ECUs) to provide more convenient and safe services for drivers. These ECUs are required to share the various control data for driving using the CAN (Controller Area Network) protocol that is one of the communication protocols for in-vehicle networks. Recently, as vehicles become capable of real-time, two-way communication with various elements on the road, modern vehicles faces a wide range of security issues. The in-vehicle networks are already suffered from the cyber-attacks exploiting newly exposed security vulnerabilities. However, it cannot guarantee the safety and security of the vehicles from as the CAN is designed without considering security. Therefore, this paper proposes a machine learning-based cyber-attack detection system (CADS) in CAN to ensure driving safety. This CADS learns the data pattern of the CAN frames generated while the driving and is able to distinguish between regular messages and cyberattack messages. The performance of the fully trained detection model of the CADS is evaluated using a vast number of testing dataset. The results show that our CADS in terms of accuracy, precision, recall, and F1 score has 100% of the detection rate for regular messages, replay attacks, brute-force attacks except for modification messages.

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Testing Intelligent Traffic Control Solutions efficiency in reducing traffic and pollution in Tirana

Testing Intelligent Traffic Control Solutions efficiency in ...

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2024 International Workshop on Quantum and Biomedical Applications, Technologies, and Sensors, Q-BATS 2024

作者： Musta, Eugen Elmazi, Donald Elmazi, Klea Mehmeti, Fatjon Hidri, Florenc Department of Finance Canadian Institute of Technology Tirana Albania Department of Computer Engineering University Metropolitan Tirana Tirana Albania Dept. of Information Technology Innovation Universiteti Luarasi University of Rijeka Tirana Albania Department of Software Engineering Canadian Institute of Technology Tirana Albania

ISBN: (纸本)9798331516390

Urban traffic congestion is an issue that metropolitan regions face throughout the world. It has a substantial negative influence on the levels of pollution in the environment and lowers the standard of living in urban areas. This study examines the efficacy of three different traffic control methods in the urban environment of Tirana, Albania: conventional static, actuated, and delay-based. The study assesses how effective they are in controlling traffic flow and reducing emissions by using simulation approaches. Using the Simulation of Urban Mobility (SUMO) tool, the investigation compares the performance of these systems, focusing on key indicators such as time spent in traffic, fuel consumption, and emission levels. Contrary to initial assumptions, the simulations reveal the actuated system as the most efficient, showcasing its superior adaptability to real-time traffic conditions at multiple consecutive intersections. This adaptability optimizes traffic flow and contributes to a reduction in environmental impact. This discovery emphasizes the significance of implementing dynamic traffic management solutions tailored to the specific dynamics of urban environments. Based on these findings, the study advocates for the adoption of actuated traffic management systems in cities struggling with similar traffic challenges, highlighting their potential to bolster urban mobility and sustainability. The research also advocates for further exploration into the scalability of these systems and their integration into broader smart city frameworks. By contributing to the ongoing discourse on urban traffic management, this study provides valuable insights that can inform policy-making and the deployment of intelligent traffic solutions. ©2024 IEEE.

关键词： Traffic congestion

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An Architectural Approach for Enhanced Data Interoperability Across Building Systems 7

An Architectural Approach for Enhanced Data Interoperability...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Layth Rafea Hazim Oguz Ata Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models’ robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications.

关键词： Training Logistic regression Ethics Accuracy Text categorization Writing Transformers Data models Feedforward neural networks Artificial intelligence

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Towards an Automatic Construction of Simulation Scenarios: A Systematic Review 23

Towards an Automatic Construction of Simulation Scenarios: A...

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Proceedings of the Winter Simulation Conference

作者： Christopher W. H. Davis Antonie J. Jetter Philippe J. Giabbanelli Microsoft USA Dept. of Engineering & Technology Management Portland State University USA Dept. of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.

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Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal

Chaos Based Pixel Scrambling Algorithm Based On Corner Trave...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Cemile İnce Kenan İnce Davut Hanbay Computer Engineering Dept Inönü University Malatya Türkiye Software Engineering Dept. Inönü University Malatya Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version.

关键词： Performance evaluation Chaos Correlation Sensitivity Data processing Encryption Internet of Things Artificial intelligence Random sequences

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Efficient Design for Multi-user Downlink Beamforming with Reconfigurable Intelligent Surface

arXiv

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arXiv 2023年

作者： Ebrahimi, Mohammad Dong, Min The Dept. of Electrical Computer and Software Engineering Ontario Tech University ON Canada

This paper considers downlink multi-user transmission facilitated by a reconfigurable intelligent surface (RIS). First, focusing on the multi-group multicast beamforming scenario, we develop a fast and scalable algorithm for the joint base station (BS) and RIS beamforming optimization to minimize the transmit power subject to the user quality-of-service (QoS) constraints. By exploring the structure of this QoS problem, we show that the joint beamforming optimization can be naturally decomposed into a BS multicast beamforming QoS problem and an RIS passive multicast beamforming max-min-fair (MMF) problem. We propose an alternating multicast beamforming (AMBF) algorithm to solve the two subproblems alternatingly. For the BS QoS subproblem, we utilize the optimal multicast beamforming structure to obtain the BS beamformers efficiently. Furthermore, we reformulate the challenging RIS MMF subproblem and employ a first-order projected subgradient algorithm (PSA), which yields closed-form updates. The computational complexity of the AMBF algorithm grows linearly with the number of RIS elements and BS antennas. We further show that the AMBF approach is also an efficient method for the RIS-assisted downlink multi-user unicast beamforming problem, providing semi-closed-form updates. Next, we study the MMF problem for the RIS-assisted downlink beamforming design and propose a PSA-based fast algorithm to compute the BS and RIS beamforming solutions with closed-form updates per iteration, leading to a highly computationally efficient solution. Simulation results show the efficacy of our proposed algorithms in both performance and computational cost compared to other alternative methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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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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Sustainability of Machine Learning-based Android Malware Detection Using API calls and Permissions 5

Sustainability of Machine Learning-based Android Malware Det...

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Cho, Woosang Lee, Hojun Han, Sangchul Hwang, Youngsup Cho, Seong-Je Dept. of Software Science Dankook University Gyeonggi-do Yongin-si16890 Korea Republic of Dept. of Computer Engineering Konkuk University Chungju Korea Republic of Dept. of Computer Science and Engineering Sun Moon University Asan Korea Republic of Dept. of Sofware Science Dankook University Yongin Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

As the Android platform and malicious apps continue to evolve, most existing Android malware detection techniques using machine learning are turning out to be unsustainable. In this paper, we propose machine learning-based Android malware detection techniques which uses both API calls and permissions as a feature set. These features are complementary and are often used to detect malicious apps. We first analyze whether a 'yearly dataset-based trained classifier' (YDataC) is sustainable or not. The 'yearly dataset-based trained classifier' refers to the classifier that learns from 80% of the dataset of a specific year from 2014 to 2021, and is tested with 20% of the datasets of every year between 2014 and 2021. Through experiments, we discovered that the classification rate has dropped significantly since 2019, and something big has changed. Therefore, the 'yearly dataset-based trained classifier' is judged to be unsustainable. Next, we present and evaluate two incremental learning methods for gradual training: an incrementally trained Random Forest (RF) and an incrementally trained Neural Network (NN). Evaluation results show that two incremental learning classifiers have better sustainability than the 'yearly dataset-based trained classifier'. The incrementally trained RF has better sustainability than the incrementally trained NN in terms of given metrics such as f1 and AUT (Area under Time). © 2022 IEEE.

关键词： Android malware

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Deep Reinforcement Learning Algorithms for Option Hedging

arXiv

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arXiv 2025年

作者： Neagu, Andrei Godin, Frédéric Kosseim, Leila Dept. of Computer Science and Software Engineering Concordia University Montréal Canada Dept. of Mathematics & Statistics Concordia University Montréal Canada

Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging;Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment. © 2025, CC BY.

关键词： Learning algorithms

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