13 research outputs found
Cyber security analysis of connected vehicles
\ua9 2024 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.The sensor-enabled in-vehicle communication and infrastructure-centric vehicle-to-everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust-based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area
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Securing mobile edge computing using hybrid deep learning method
In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques
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A systematic review on millimeter-wave hybrid beamforming for wireless intelligent transport systems
As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input-multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITS). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook and reflecting intelligent surfaces to optimizes system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions and future research directions are provided so that systematic review should serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state-of-the-art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations
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QoE-based assignment of EVs to charging stations in metropolitan environments
With the recent advances in battery technology enabling fast charging, public Charging Stations (CSs) are becoming a viable choice for Electric Vehicles (EVs). However , the distribution of EVs relies on strategic assignment of EVs to CSs. EVs drivers' Quality of Experience (QoE) is an significant impact factor that should be considered to find the optimal assignment of EVs to CSs. In this context, a novel framework to find the optimal assignment of EVs to CSs has been proposed based on optimization of QoE. Our proposed approach considers the travel time of EVs towards CSs taking into account the distance between EVs and CSs, the impact of congestion level on the roads resulted from the Internal Combustion Engine Vehicles (ICEVs) and EVs, queuing time at the CSs, and the time required to fully charge the EVs battery when connected to any charging slot at a CSs. The adjacency between the different zones in a city environment is also considered in order to minimize the potential number of CSs for each EVs. Specifically, the assignment problem is formulated as Mixed Integer Nonlinear Programming (MINLP), and a heuristic solution is developed using the Genetic Algorithm (GA) technique. The performance evaluation in realistic metropolitan environment attests the benefits of the proposed CSs assignment framework considering range of charging metrics
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MoBShield: a novel XML approach for securing mobile banking
Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application's intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training datasets. The dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity, University of New Brunswick and benign apps from Google Play. A machine learning algorithm is used to determine whether a mobile banking application is malicious based on its permission requests. Further, an eXplainable machine learning (XML) approach is developed to improve trust by explaining the reasoning behind the algorithm’s behaviour. Performance evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false positives. Specifically, the adapted artificial neural networks (ANN), convolutional neural networks (CNN) and XML approaches achieve a higher accuracy of 99.7% and the adapted deep neural networks (DNN) approach achieves 99.6% accuracy in comparison with the state-of-the-art approaches. These promising results position the proposed approach as a potential tool for real-world scenarios, offering a robust means of identifying and thwarting malware in mobile-based banking applications. Consequently, MoBShield has the potential to significantly enhance the security and trustworthiness of mobile banking platforms, mitigating the risks posed by cyber threats and ensuring a safer user experience
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Cyber security analysis of connected vehicles
The sensor-enabled in-vehicle communication and infrastructure-centric vehicle-to-everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust-based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area
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Enhancing reliability and stability of BLE mesh networks: a multipath optimized AODV approach
Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in Wireless Ad-Hoc Networks (WAHN) is forwarding-based and therefore more efficient, with lower overheads. However, the Packet Delivery Ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose a Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR, and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in case of node failures, compared with other forwarding-based protocols which require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments