Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles

Grover, Harsh; Alladi, Tejasvi; Chamola, Vinay; Singh, Dheerendra; Choo, Kim‐Kwang Raymond

doi:10.1109/jiot.2021.3071362

Cited by 35 publications

(8 citation statements)

References 17 publications

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“…According to the table (1), we observe that all the considered methods used supervised ML techniques (e.g., [19,20]), except the work [2].…”

Section: Summary and Discussionmentioning

confidence: 99%

“…The other ML-based attack detection methods described in [22,3,29,25,26] can distinguish between a normal traffic or abnormal one (i.e., attack) in a binary way without identifying the specific type of detected attack. Note that the work [2] used unsupervised task for attack detection. Some works did not explain the methodology and the parameters of the ML models ( [28,29,24,22,20,3].…”

Section: Summary and Discussionmentioning

confidence: 99%

“…In the same context for position falsification attacks detection, the authors of [2] proposed an unsupervised based-MDS. In this study, the VeReMi dataset [12] was used to train three different DL models.…”

Section: Evolution Of Ml-based Mds Technologymentioning

confidence: 99%

“…However, cryptographic solutions are insufficient to protect the VANET network against internal attackers since they are already authenticated in the considered network. Alternatively, several attacks and Misbehavior Detection System (MDS) based on Machine Leaning (ML) have been proposed [2,3] to secure VANET networks from different types of attacks. In the VANET context, many pieces of information, like position, speed, acceleration, and direction of a vehicle, increase the complexity of such systems with opposite to the classical intrusion detection systems (IDSs) of computer networks.…”

Section: Introductionmentioning

confidence: 99%

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A review and a tutorial of ML-based MDS technology within a VANET context: From data collection to trained model deployment

Marouane¹,

Dandoush²,

Amour³

et al. 2023

Preprint

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<p>Recent advances in telecommunication and machine learning (ML) have allowed for new smart and autonomous vehicle applications to improve road safety, environmental conditions, and traffic management through Vehicle to Vehicle (V2V) or Vehicle to Infrastructure (V2I) communication. However, with the rise of advanced cyber-attacks, the authenticity of a message guarantees its source but not its correctness. To mitigate the new sophisticated attacks, new Misbehavior Detection Systems (MDS), that use machine learning algorithms to detect misbehaving vehicles, have been proposed. This work provides first a comprehensive review of recent developments in ML-based MDS technology within a Vehicular Ad-Hoc Network (VANET) context, covering data collection, feature selection, model training, model evaluation and deployment. We survey useful public datasets and summarize recent studies. We report useful pieces of information for every work. In particular, we highlight the considered dataset for ML training, list the selected ML models, indicate the feature selection and dimensionality reduction techniques, recapitulate the main results, report the performance metrics and mention the deployment guidelines when applicable. Then, we compare the surveyed studies discussing not only the strength points but also their limitations. One of the key observations from the surveyed works is the absence of a quantitative analysis of the proposed models' execution time, which is a crucial performance metric considering the limited on-board and edge computing resources. To develop a feasible ML-based MDS for V2X communication, it is essential to address this issue and propose a deployment strategy that optimizes the allocated resources for this technology. However, achieving this remains a challenge.</p> <p>Moreover, in view of the fact that data generation and analysis are critical phases in this technology. Also, using simulation has many advantages over the real data collection, we provide a tutorial on how realizing a useful dataset collection with popular open-source tools while considering exemplar types of attacks. Last, we demonstrate through the tutorial the use of the collected dataset for ML-based MDS model selection and training. An open source github repository is provided for regenerating the whole explained scenarios and modify according to the given research issue.</p>

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“…According to the table (1), we observe that all the considered methods used supervised ML techniques (e.g., [19,20]), except the work [2].…”

Section: Summary and Discussionmentioning

confidence: 99%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Evolution Of Ml-based Mds Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A review and a tutorial of ML-based MDS technology within a VANET context: From data collection to trained model deployment

Marouane¹,

Dandoush²,

Amour³

et al. 2023

Preprint

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“…Assim como elencado em [Grover et al 2021], as soluc ¸ões modernas de computac ¸ão raramente são resolvidas em apenas um nível, pois suas demandas possuem um alto grau de exigência tanto de potência computacional, quanto de latência e confiabilidade. Assim, a fim de prover suporte para o algoritmo DISMISS-BSM, visualizou-se a utilizac ¸ão de uma arquitetura multinível, fracionando a responsabilidade pelas tarefas ao longo de diferentes níveis computacionais, frequentemente combinando computac ¸ão em nuvem, computac ¸ão de borda, computac ¸ão local e dispositivos IoT para sustentar os servic ¸os centrados no usuário.…”

Section: Arquitetura Multinívelunclassified

Detecção incremental de comportamento malicioso em VANETs

Dutra¹,

Bonfim²,

Travagini³

et al. 2022

Anais Do XXII Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2022)

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Falsificação de posição em C-ITS pode causar efeitos desastrosos no trânsito, sendo um problema em aberto na literatura. Este trabalho apresenta o DISMISS-BSM, um algoritmo capaz de identificar os ataques de falsificação de posição, utilizando dois novos atributos preditores e uma nova forma de agrupamento das mensagens em janelas deslizantes de análise de tamanho incremental, aliando agilidade e eficácia na detecção. Para avaliação do desempenho foram utilizados cinco modelos de aprendizado de máquina. Destacam-se o desempenho na detecção dos ataques random offset, superando os demais trabalhos da área, e o custo-benefício do modelo Decision Tree, com média de tempo de treinamento e inferência, de 47,617 e 0,085 seg, respectivamente.

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Secure and lightweight message dissemination framework for internet of vehicles

Bodkhe,

Tanwar

2024

Security and Privacy

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The Internet of Vehicles (IoV) revolutionizes vehicle communication in dynamic networks. Message dissemination in IoV involves sharing critical information for the safety and convenience of the IoV network. It is very crucial to secure message dissemination due to potential cyber‐attacks, traffic disruptions, and privacy breaches. Data integrity, authentication, and privacy are vital to maintaining trust and safety in the IoV network. This network consists of resource‐constrained IoV devices with limited resources due to the availability of embedded components in vehicular systems. Therefore, optimizing algorithms and protocols is crucial for efficient vehicle‐to‐everything (V2X) communication, enhancing safety and transportation efficiency. Solutions often include lightweight protocols and secure message exchange. This paper proposes a machine learning (ML) based secure and lightweight message dissemination framework for resource‐constrained IoV. First, we present an ML‐based threat classification model capable of effectively categorizing adversarial and nonadversarial data streams and delivering an optimized model with superior accuracy. Furthermore, we also propose a secure message dissemination scheme using lightweight cryptographic primitives, which significantly reduces computational, communication, and energy overhead. To validate the robustness of our proposed ML‐based secure and lightweight message dissemination framework, we evaluate it using various security parameters and performance measures such as computation cost, communication cost, energy cost, accuracy, precision, recall, and F1‐score. Our contributions promise to significantly enhance the security and efficiency of message dissemination in IoV environments and advance lightweight, secure, and reliable transportation systems for the future.

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Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles

Cited by 35 publications

References 17 publications

A review and a tutorial of ML-based MDS technology within a VANET context: From data collection to trained model deployment

A review and a tutorial of ML-based MDS technology within a VANET context: From data collection to trained model deployment

Detecção incremental de comportamento malicioso em VANETs

Secure and lightweight message dissemination framework for internet of vehicles

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