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Security experimental framework of trajectory planning for autonomous vehicles

International Journal of Intelligent Networks 2024年第1期5卷 315-324页

作者： Al-sheyab, Sujoud Al-shara, Zakarea Al-khaleel, Osama Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan Department of Software Engineering Jordan University of Science and Technology Irbid Jordan

In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the Trajectory Privacy Attack on Autonomous Driving (T-PAAD), underscores the urgency for robust security measures. Unfortunately, existing simulations for preemptively assessing the T-PAAD attack's impact are scarce. This paper introduces the Security Experimental Framework for Autonomous Vehicles (SEFAV), designed to address this gap by providing a versatile platform for simulating security scenarios in AV environments. SEFAV is cross-platform and compatible with different operating systems such as Windows and Linux, enhancing accessibility for researchers and practitioners. Our primary focus lies in showcasing the T-PAAD attack within our framework, highlighting its efficacy in evaluating and fortifying AV security. © 2024 The Authors

关键词： Vehicle routing

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A parallel hybrid approach integrating clonal selection with artificial bee colony for logistic regression in spam email detection

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Neural Computing and Applications 2024年 1-19页

作者： Dedeturk, Bilge Kagan Akay, Bahriye Department of Software Engineering Erciyes University Kayseri Melikgazi38039 Turkey Department of Computer Engineering Erciyes University Kayseri Melikgazi38039 Turkey

Spam emails are sent to recipients for advertisement and phishing purposes. In either case, it disturbs recipients and reduces communication quality. Addressing this issue requires classifying emails on servers as either spam or ham. Numerous methods have been proposed for this classification task. Among them, logistic regression (LR) stands out for its simplicity, speed, and ease of implementation. However, LR suffers from low detection rates caused by the gradient descent algorithm used in its training phase. To overcome this limitation, we propose a novel method based on the clonal selection algorithm (CSA), renowned for its success in optimization problems due to its local and global search capabilities. Despite CSA’s effective optimization performance, it suffers from robustness and slow training time. Therefore, the CSA and artificial bee colony (ABC) algorithms are hybridized to improve CSA’s robustness and are parallelized to reduce the training time significantly. This hybrid method is employed to optimize the weights of LR by minimizing the cost at the output of LR. The empirical results denote that the proposed method, named CSA–ABC–LR, yields better classification performance compared to state-of-the-art models reported by previous studies, demonstrating an accuracy rate of 99.13% on the Enron-1 dataset, 99.22% on the CSDMC2010 dataset, and 94.49% on the Spambase dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Artificial bee colony Clonal selection algorithm Email spam detection Logistic regression Spam filtering

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A multi-mode real-time system verification model using efficient event-driven dataset

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1187-1200页

作者： Islam, Nayreet Azim, Akramul Department of Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa Canada

Real-time systems experience many safety and performance issues at run time due to different uncertainties in the environment. Systems are now becoming highly interactive and must be able to execute in a changing environment without experiencing any failure. A real-time system can have multiple modes of operation such as safety and performance. The system can satisfy its safety and performance requirements by switching between the modes at run time. It is essential for the designers to ensure that a multi-mode real-time system operates in the expected mode at run time. In this paper, we present a verification model that identifies the expected mode at run time and checks whether the multi-mode real-time system is operating in the correct mode or not. To determine the expected mode, we present a monitoring module that checks the environment of the system, identifies different real-world occurrences as events, determines their properties and creates an event-driven dataset for failure analysis. The dataset consumes less memory in comparison to the raw input data obtained from the monitored environment. The event-driven dataset also facilitates onboard decision-making because the dataset allows the system to perform a safety analysis by determining the probability of failure in each environmental situations. We use the probability of failure of the system to determine the safety mode in different environmental situations. To demonstrate the applicability of our proposed scheme, we design and implement a real-time traffic monitoring system that has two modes: safety, and performance. The experimental analysis of our work shows that the verification model can identify the expected operating mode at run time based on the safety (probability of failure) and performance (usage) requirements of the system as well as allows the system to operate in performance mode (in 3295 out of 3421 time intervals) and safety mode (in 126 out of 3421 time intervals). The experimental resul

关键词： Real time systems

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Makespan and energy based virtual machine scheduling in cloud systems

Journal of the Faculty of Engineering and Architecture of Ga...

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Journal of the Faculty of engineering and Architecture of Gazi University 2024年第3期39卷 1661-1672页

作者： Kizil, Alper Karabulut, Korhan Department of Computer Engineering Faculty of Engineering Yasar University Izmir Bornova35100 Turkey Department of Software Engineering Faculty of Engineering Yasar University Izmir Bornova35100 Turkey

Cloud computing is one of the newest computing paradigms that emerged after worldwide development of Internet infrastructure. It is a technology that saves both large companies and small and medium scale companies as well as independent developers from the cost of keeping infrastructure hardware up to date, and operational while also providing flexibility on resource use as well providing additional opportunity to minimize data losses. While in the future, it is evident that demand for cloud computing will be on the rise. These kinds of datacenters, due to their nature, consume large amounts of energy and even the savings on smallest scales will enable these gigantic centers to save a significant amount of energy in total. If we have a look at the literature, we can see green computing is gaining immense popularity over the years. The Cloud Scheduling problem is a proven problem to be NP-Hard, aiming to find the best solution for a limited number of cloud resources, which could theoretically be serving an unlimited number of user demands. In this study, firstly, an experimental workload / power consumption model is proposed for a server computer, and then two genetic algorithms optimizing makespan and energy consumption are compared on these metrics at different server loads. As a result, it has been seen that these two criteria are closely related to each other, and it has been determined that optimizing the energy criterion has a more positive effect between 10% and 13% compared to the time criterion optimization at full or near full server loads. In this way, it has been shown that significant energy savings can be achieved by using energy optimization as an objective function at high server loads. © 2024 Gazi Universitesi. All rights reserved.

关键词： Energy utilization

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Ensemble-based Robust Model for Accurate Driving Range Estimation of EVs Leveraging Big Data 8

Ensemble-based Robust Model for Accurate Driving Range Estim...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Hosseini, Samira Yassine, Abdulsalam Akilan, Thangarajah Department of Electrical and Computer Engineering Canada Department of Software Engineering Canada Department of Software Engineering Lakehead University Thunder Bay Canada

ISBN: (纸本)9798350382150

In the emergence of greener transportation, electric vehicles (EVs) play an important role, where the accurate prediction of the driving range is pivotal for alleviating driver range anxiety, serving as a foundation for spatial planning, operational strategies, and efficient charging infrastructure management. This study addresses the challenge of limited driving range in EVs and introduces an ensemble-based machine learning (ML) model for predicting the vehicle’s driving range. By employing big data collected from the real world, this work investigates the appropriateness of various ML models, including extreme learning model (ELM), extreme gradient boosting (XGBoost), multiple linear regression (MLR), multilayer perceptron (MLP), deep MLP, random forests (RF), AdaBoost, and support vector regression (SVR) for the problem stated above. Extensive experimental analysis proves that the ensemble framework that exploits MLR and XGBoost predictors surmounts the existing solutions. It achieves an R2 score greater than 0.9 on both training and testing data subsets, exhibiting no overfitting issues, and boasting acceptable inference time. The findings offer a compelling solution for enhancing the estimation of EV driving range for practical applications. ©2024 IEEE.

关键词： Forecasting

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A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

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computer Systems Science & engineering 2023年第4期45卷 857-868页

作者： Mohemmed Sha Department of Software Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The use of Amazon Web Services is growing rapidly as more users are adopting the *** has various functionalities that can be used by large corporates and individuals as *** analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related *** this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter *** data is managed to the EC2 by using elastic load *** collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative *** accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

关键词： Sentiment analysis AWS cloud data analytics machine learning

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Dynamic Synergy: Leveraging RIS and Reinforcement Learning for Secure, Adaptive Underlay Cognitive Radio Networks 15

Dynamic Synergy: Leveraging RIS and Reinforcement Learning f...

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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Tashman, Deemah H. Cherkaoui, Soumaya Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada

ISBN: (纸本)9798331520113

Reconfigurable intelligent surfaces (RISs) have recently been employed to facilitate communication and improve performance by reflecting signals through configuring phase shifts toward the intended destination. This article examines the physical layer security of an underlay cognitive radio network aided by an RIS and in the presence of multiple eavesdroppers. The research is conducted under practical conditions, encompassing RIS hardware constraints and cascaded fading channels. An optimization problem is proposed with the objective of maximizing the secrecy rate of secondary users by optimizing the reflection angles of the RIS and the transmission power of the secondary user transmitter. A deep reinforcement learning method, specifically the soft actor-critic, is presented as a solution. The results section demonstrates the effect of altering the number of RIS elements on security. We also analyze the impact of hardware limitations and cascade levels on the secrecy rate. The effect of varying the number of eavesdroppers and the maximum permissible transmission power are also examined. © 2025 IEEE.

关键词： Fading channels

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An effective binary dynamic grey wolf optimization algorithm for the 0-1 knapsack problem

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Multimedia Tools and Applications 2024年 1-33页

作者： Erdoğan, Feyza Karakoyun, Murat Gülcü, Şaban Department of Software Engineering Konya Technical University Konya Turkey Department of Computer Engineering Necmettin Erbakan University Konya Turkey

Metaheuristic algorithms are recommended and frequently used methods for solving optimization problems. Today, it has been adapted to many challenging problems and its successes have been identified. The grey wolf optimizer (GWO) is one of the most advanced metaheuristics. Because of the advantages it provides, GWO has been applied to solve many different problems. In this study, a new variant of GWO, the Binary Dynamic Grey Wolf Optimizer (BDGWO), is proposed for the solution of binary optimization problems. The main contributions of BDGWO compared to other binary GWO variants are that it uses the XOR bitwise operation to binarize and is based on the dynamic coefficient method developed to determine the effect of the three dominant wolves (alpha, beta, and delta) in the algorithm. BDGWO is a simple, feasible, and successful method that strikes a balance between local search and global search in solving binary optimization problems. To determine the success and accuracy of the proposed BDGWO, it was tested on the 0-1 knapsack problem (0-1 KP), which is classified as an NP-Hard problem. The BDGWO was compared with 17 different binary methods across a total of 55 data sets from three different studies published in the last four years. The Friedman test was applied to interpret the experimental results more easily and to evaluate the algorithm results statistically. As a result of the experiments, it has been proven that the BDGWO is an effective and successful method in accordance with its purpose. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optimization algorithms

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Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

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computer Systems Science & engineering 2023年第5期45卷 2141-2154页

作者： Prohim Tam Sa Math Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge *** offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth *** partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop *** optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation *** reward formulation primarily considers taskrequired computing resources and action-applied allocation *** defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated *** simulation for the control rule installation is conducted using Mininet and Ryu SDN *** delay and task delivery/drop ratios are used as the key performance metrics.

关键词： Deep learning partial task offloading software-defined networking virtual machine virtual network functions

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A Communication-Based Solution to Detect Islanding using Correlation Element in Distributed Generation Environment

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Distributed Generation and Alternative Energy Journal 2025年第2期40卷 427-456页

作者： Ranjan, Sanjeev Kumar, Munna Kumar, Jitendra Mahanty, R.N. Sood, Vijay K. Department of Electrical Engineering NIT Jamshedpur Jharkhand 831014 India Department of Electrical Engineering ARMIET Maharashtra Thane421601 India Department of Electrical Computer and Software Engineering Ontario Tech University ON Canada

Issues regarding safety, circuit breaker reclosing, power quality, and regulatory compliance are identified when islanding is to be detected in a microgrid. In this paper, a novel communication-based, passive islanding detection method (IDM) is proposed to identify islanding in a microgrid to address these issues. This proposed method is based on correlation using the impedance measurement at the point of common coupling (PCC) and distributed generation (DG). The methodology is validated on a modified IEEE-13 bus system through a Phasor Measurement Unit (PMU) with a set threshold to discriminate between islanding and non-islanding events. The benefits of this proposed method are fast and accurate islanding detection. This IDM can tackle all the concerns regarding islanding detection in the cases of active power mismatch (APM), reactive power mismatch (RPM), DG disconnection with the presence of noise, unbalanced loads, irradiance change, weak and/or strong grid without providing any false signal as per IEEE UL1741 and IEEE STD. 929-2000. The authentication of the proposed scheme is also carried out for non-islanding events such as altered faults, non-linear loads, load switching, capacitor and inductor switching, feeder disconnection, and motor swapping, where all tests endorse the applicability of the proposed technique. The proposed methodology is validated both with simulation and Opal-RT laboratory results. © 2025 River Publishers.

关键词： Electric impedance measurement

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