Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

Junejo, Muhammad Haleem; Rahman, Ab Al-Hadi Ab; Shaikh, Riaz Ahmed; Yusof, Kamaludin Mohamad; Kumar, Dileep; Memon, Imran

doi:10.1016/j.procs.2021.10.058

Cited by 13 publications

(2 citation statements)

References 42 publications

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“…The study in [29] used C4.5, a decision tree algorithm, for IDS development. In [30], a VANET security model is proposed that works in two stages: The trust estimation model, which adapts ML algorithms, and the decision model. In [31], an IDS is proposed based on a multivariate statistical approach and k-nearest neighbor (KNN) algorithm, which can detect attacks and alleviate their effect where possible.…”

Section: Background Studies and Literature Reviewmentioning

confidence: 99%

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

Dhar,

Roy,

Akhand

et al. 2023

Electronics

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Vehicular ad hoc networks (VANETs) incorporating vehicles as an active and fast topology are gaining popularity as wireless communication means in intelligent transportation systems (ITSs). The cybersecurity issue in VANETs has drawn attention due to the potential security threats these networks face. An effective cybersecurity measure is essential as security threats impact the overall system, from business disruptions to data corruption, theft, exposure, and unauthorized network access. Intrusion detection systems (IDSs) are popular cybersecurity measures that detect intrusive behavior in a network. Recently, the machine learning (ML)-based IDS has emerged as a new research direction in VANET security. ML-based IDS studies have focused on improving accuracy as a typical classification task without focusing on malicious data. This study proposes a novel IDS for VANETs that offers more attention to classifying attack cases correctly with minimal features required by applying principal component analysis. The proposed Cascaded ML framework recognizes the difference between the attack and normal cases in the first step and classifies the attack data in the second step. The framework emphasizes that an attack should not be classified into the normal class. Finally, the proposed framework is implemented with an artificial neural network, the most popular ML model, and evaluated with the Car Hacking dataset. In addition, the study also investigates the efficiency of typical classification tasks and compares them with results of the proposed framework. Experimental results on the Car Hacking dataset have revealed the proposed method to be an effective IDS and that it outperformed the existing state-of-the-art ML models.

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Section: Background Studies and Literature Reviewmentioning

confidence: 99%

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

Dhar,

Roy,

Akhand

et al. 2023

Electronics

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“…The papers in Ref. [20,21] calculate the trustworthiness between nodes by the trust model to reduce the possibility of MiTM attackers in Vehicular Ad-Hoc Networks, which is inspiring for the process of data dissemination in our privacy-protection scheme. Therefore, we borrow the idea of the trust model to form the evaluation of opposite side gadgets gradually and dynamically in the process of continuous interaction between gadgets to guide the dissemination behaviour of gadgets.…”

Section: Data Dissemination Process Centred On the Security Of The Ma...mentioning

confidence: 99%

R‐D ^P : A risk‐adaptive privacy protection scheme for mobile crowdsensing in industrial internet of things

Shuai

Zhang

Cao

et al. 2022

IET Information Security

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The integration of the Mobile Crowdsensing (MCS) and Industrial Internet of Things (IIoT) brings enormous volumes of data that generate significant commercial value. However, the data contain a wealth of sensitive information about devices' environmental situation and collective activities, which draws a flock of adversaries and poses an unprecedented security challenge. Furthermore, sensing gadgets deployed in the IIoT device are usually resource-constrained and often do not have adequate 3C resources (i.e. communication, computing, caching) to run sophisticated privacy-preserving methods, making them easier targets for attacks in data sharing. Therefore, a risk-adaptive privacy protection scheme R-D P for MCS-enabled IIoT gadgets is proposed, which comprises a closed-loop risk-awareness process and an adaptive privacy protection method D P (a dissemination process with perturbation). The closed-loop process dynamic awareness of risks and threats in MCS task feeds appropriate privacy protection advice to the decisionmakers for the task. In addition, D P was designed as a lightweight and risk-adaptive privacy protection method to meet the operational needs of 3C resource-constrained gadgets. The analysis and evaluation show that R-D P provides satisfactory privacy protection while the availability of statistical features reaches more than 96%, and the time complexity is only O (1) for sensing gadgets.

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Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

Swamynathan,

Shanmugam,

Mohan Kumar

et al. 2024

Trans Emerging Tel Tech

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Vehicular ad hoc networks (VANETs) in portable broadband networks are a revolutionary concept with enormous potential for developing safe and efficient transportation systems. Because VANETs are open networks that require regular information sharing, it might be difficult to ensure the security of data delivered through VANETs as well as driver privacy. This paper proposes a blockchain technology that supports trusted routing and deep learning for traffic prevention and security enhancement in VANETs. Initially, the proposed Feature Attention‐based Extended Convolutional Capsule Network (FA_ECCN) model predicts the driver's behaviors such as normal, drowsy, distracted, fatigued, aggressive, and impaired. Next, the Binary Fire Hawks‐based Optimized Link State Routing Protocol (BFH_OLSRP) is used to route traffic after trust values have been assessed. Furthermore, Binary Fire Hawks Optimization (BFHO) determines the best routing path based on criteria such as link stability and node stability degree. Finally, blockchain storage is supported by the Interplanetary File System (IPFS) technology to improve the security of VANET data. Additionally, the validation process is established by using Delegated Practical Byzantine Fault Tolerance (DPBFT). As a result, the proposed study employs the blockchain system to securely send data to neighboring vehicles via trust‐based routing, thereby accurately predicting the driver's behavior. The proposed method achieves a better outcome in terms of latency, packet delivery ratio (PDR), overhead packets, throughputs, end‐to‐end delay, transmission overhead, and computational cost. According to simulation results and efficiency evaluation, the proposed approach outperforms existing approaches and enhances vehicle communication security in an effective manner.

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

Cited by 13 publications

References 42 publications

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

R‐D ^P : A risk‐adaptive privacy protection scheme for mobile crowdsensing in industrial internet of things

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

Cited by 13 publications

References 42 publications

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET

R‐D P : A risk‐adaptive privacy protection scheme for mobile crowdsensing in industrial internet of things

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

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R‐D ^P : A risk‐adaptive privacy protection scheme for mobile crowdsensing in industrial internet of things