An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

Rashid, Kanwal; Saeed, Yousaf; Ali, Abid; Jamil, Faisal; Alkanhel, Reem; Muthanna, Ammar

doi:10.3390/s23052594

Cited by 30 publications

(12 citation statements)

References 45 publications

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“…Rashid et al 32 provides an adaptive real‐time machine learning‐based malicious node detection framework in VANETs. The framework uses machine learning algorithms to identify malicious nodes in VANETs in real time.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous vehicle security: Current survey and future research challenges

Pali,

Amin,

Abdussami

2024

Security and Privacy

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SummaryThe advancement in technology and the adoption of innovative developments have made our lives easier. Every industry, from agriculture to transport, has experienced this advancement. The entire transportation system is in the process of becoming intelligent. Smart mobility of autonomous vehicles is an important part of the upcoming smart cities. The autonomous vehicle provides functionalities for safety and convenience. It has been an emerging field with a lot of attention and still under advancement and requires a thorough study and solutions for potential challenges and opportunities. Despite impressive advantages, it has some security vulnerabilities which could lead to life‐threatening consequences. Though they could prevent road accidents by eliminating human errors from the driving process, they also create the possibility of cyber‐physical attacks on the vehicular controlling system. For this, many researchers have focused on the security attacks and defenses in autonomous vehicular systems. A 360‐degree research has been undertaken to understand the concept of autonomous vehicles entirely, such as history, social impacts, difficulties and challenges, current product developments, and security aspects. Also, a vehicular architecture is proposed by integrating various technologies such as 5G, SDN, cloud, and so forth, its possible security attacks are mentioned.

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Section: Literature Reviewmentioning

confidence: 99%

Autonomous vehicle security: Current survey and future research challenges

Pali,

Amin,

Abdussami

2024

Security and Privacy

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“…Rashid et al [19] have presented a machine learning-based approach for detecting rogue nodes in real time. A classification with GBT, LR, MLPC, and SVM replicas were used to test the performance of our projected classifier in OMNET++ and SUMO.…”

Section: Related Workmentioning

confidence: 99%

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

Thirumalraj,

2023

Preprint

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Vehicle-to-vehicle (V2V) communication enables a network of automobiles to perform collaborative computing, giving rise to the concept of a "vehicular cloud" (VC). However, without the need for edge nodes or cloud servers, vehicles can carry out applications needing the massive amount of processing cooperatively on their own by creating a Vehicular Ad-Hoc Network (VANET). Managing the recurrent topology alteration caused by vehicle mobility is a significant challenge for VANET cooperative computing. In this research, we present a V2V-based cooperative computing approach. The suggested method takes into account the distance between vehicles while choosing which ones to collaborate with, and it waits task offloading until the last possible moment to ensure a stable and energy-efficient cooperative computing environment. Despite its competitive performance when compared to other MH algorithms, the artificial rabbits optimisation (ARO) algorithm still suffers from poor accuracy and the issue of becoming trapped in solutions. By antagonism methods, this research creates selective opposition version of the artificial rabbit procedure (LARO), which eliminates the negative consequences of these shortcomings. To begin, during the random concealment phase, a Lévy flight strategy is implemented to increase population diversity and dynamics. The algorithm's convergence accuracy is enhanced by the richness of its various population samples. The tracking efficiency is improved, and ARO is kept from getting stuck in its existing local solutions by adopting the selective opposition technique. In comparison to traditional static scheduling techniques, the suggested strategy improves upon both energy efficiency and network reliability.

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“…In this research [66], the authors have posited a real-time system for detecting malicious nodes. Especially for DDoS attack detection using machine learning which includes two algorithms: Te initial approach employed in machine learning optimization is the Brayden-Fletcher-Goldfarb-Shannon (L-BFGS) method.…”

Section: Rashid Et Al's Modelmentioning

confidence: 99%

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

Nobahari,

Bakhshayeshi Avval,

Akhbari

et al. 2023

Security and Communication Networks

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The vehicle ad-hoc network (VANET) is a crucial technology that will play a significant role in shaping the future of transition systems, which is widely used as a subset of ad-hoc networks. VANET aims to ensure driver safety by establishing independent communication with nearby vehicles. A key requirement for successful data transmission is cooperation among nodes, as factors such as high mobility, limited radio range, signal fading, and noise lead to packet loss. Security issues in vehicle ad-hoc networks have recently become a major concern. One factor that affects security is the presence of abusive nodes in the network. Like selfish nodes, they are reluctant to share their sources with their neighbors and try to keep their property. The misbehavior of malicious nodes includes dissemination of false traffic information, false location information, and redirection of packets to a wrong path, retransmission of packets, impersonation, so these nodes should be tracked down to ensure the operation of the network. This article provides a complete summary of various research works proposed to detect selfish and malicious nodes and isolate them from honest vehicles. This review article first describes the types of attacks. It then presents the methods proposed by researchers to deal with uncooperative nodes and compares their performance based on parameters such as the number of misbehaving nodes detected, overhead, throughput, layer involved in the attacks.

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An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

Cited by 30 publications

References 45 publications

Autonomous vehicle security: Current survey and future research challenges

Autonomous vehicle security: Current survey and future research challenges

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

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