63 research outputs found
Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
No full text[EN] Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model's performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.This work is supported by the SC&SS, Jawaharlal Nehru University, New Delhi, India. This research is supported by the B11 unit of assessment, Centre for Computing and
Informatics Research Centre, Department of Computer Science, Nottingham Trent University, UK.Manderna, A.; Kumar, S.; Dohare, U.; Aljaidi, M.; Kaiwartya, O.; Lloret, J. (2023). Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic. Sensors. 23(21). https://doi.org/10.3390/s23218772S232
Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks
No full textRecent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption
Cybersecurity Risk Analysis of Electric Vehicles Charging Stations
No full text[EN] The increasing availability of Electric Vehicles (EVs) is driving a shift away from traditional gasoline-powered vehicles. Subsequently, the demand for Electric Vehicle Charging Systems (EVCS) is rising, leading to the significant growth of EVCS as public and private charging infrastructure. The cybersecurity-related risks in EVCS have significantly increased due to the growing network of EVCS. In this context, this paper presents a cybersecurity risk analysis of the network of EVCS. Firstly, the recent advancements in the EVCS network, recent EV adaptation trends, and charging use cases are described as a background of the research area. Secondly, cybersecurity aspects in EVCS have been presented considering infrastructure and protocol-centric vulnerabilities with possible cyber-attack scenarios. Thirdly, threats in EVCS have been validated with real-time data-centric analysis of EV charging sessions. The paper also highlights potential open research issues in EV cyber research as new knowledge for domain researchers and practitioners.This research is supported by the B11 unit of assessment, Centre for Computing and
Informatics Research Centre, Department of Computer Science, Nottingham Trent University, UK.Hamdare, S.; Kaiwartya, O.; Aljaidi, M.; Jugran, M.; Cao, Y.; Kumar, S.; Mahmud, M.... (2023). Cybersecurity Risk Analysis of Electric Vehicles Charging Stations. Sensors. 23(15). https://doi.org/10.3390/s23156716S231
Green Communication in IoT for Enabling Next-Generation Wireless Systems
No full text[EN] Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network's throughput.The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number NBU-FFR-2024-1182-02 .Aljaidi, M.; Kaiwartya, O.; Samara, G.; Alsarhan, A.; Mahmud, M.; Alenezi, SM.; Alazaidah, R.... (2024). Green Communication in IoT for Enabling Next-Generation Wireless Systems. Computers. 13(10). https://doi.org/10.3390/computers13100251S131
Optimal Placement and Capacity of Electric Vehicle Charging Stations in Urban Areas: Survey and Open Challenges
No full textPLUG: A City-Friendly Navigation Model for Electric Vehicles with Power Load Balancing upon the Grid
No full textWorldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model
B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment
No full textVehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation
EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism
No full textBackground: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment. ADHD diagnosis is primarily based on psychiatric tests, as there is currently no clinically utilized objective diagnostic tool. Nonetheless, several studies in have documented endeavors to create objective instruments designed to assist in the diagnostic process of ADHD, aiming to enhance diagnostic accuracy and reduce subjectivity. Method: This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. To capture complex patterns in EEG data, this study proposes a double-augmented attention mechanism ResNet-based model. Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique. Results: AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model’s performance. By a wide margin, the proposed ResNet model outperforms the traditional models with a 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score. Conclusions: ROC AUC score of 0.99 for the model underscores its remarkable capability to differentiate between children with and without ADHD, thereby minimizing misclassification errors and improving diagnostic precision
Comparative analysis of impact of classification algorithms on security and performance bug reports
No full textIdentification and classification of bugs, e.g., security and performance are a preemptive and fundamental practice which contributes to the development of secure and efficient software. Software Quality Assurance (SQA) needs to classify bugs into relevant categories, e.g., security and performance bugs since one type of bug may have a higher preference over another, thus facilitating software evolution and maintenance. In addition to classification, it would be ideal for the SQA manager to prioritize security and performance bugs based on the level of perseverance, severity, or impact to assign relevant developers whose expertise is aligned with the identification of such bugs, thus facilitating triaging. The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. We first label the existing dataset from the Bugzilla repository with the help of a software security expert to train the algorithms. Our research type is explanatory, and our research method is controlled experimentation, in which the independent variable is prediction accuracy and the dependent variables are ANN, SVM, NB, DT, LR, and KNN. First, we applied preprocessing, Term Frequency-Inverse Document Frequency feature extraction methods, and then applied classification algorithms. The results were measured through accuracy, precision, recall, and F-measure and then the results were compared and validated through the ten-fold cross-validation technique. Comparative analysis reveals that two algorithms (SVM and LR) perform better in terms of precision (0.99) for performance bugs and three algorithms (SVM, ANN, and LR) perform better in terms of F1 score for security bugs as compared to other classification algorithms which are essentially due to the linear dataset and extensive number of features in the dataset
An Energy Efficient Strategy for Assignment of Electric Vehicles to Charging Stations in Urban Environments
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