A Systematic Review on Intelligent Intrusion Detection Systems for VANETs

Gonçalves, Fábio; Ribeiro, Bruno; Gama, Óscar; Santos, Alexandre; Costa, António; Dias, Bruno; Macedo, Joaquim; Nicolau, Maria João

doi:10.1109/icumt48472.2019.8970942

Cited by 13 publications

(13 citation statements)

References 29 publications

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“…Based on their survey it is observed that no specific information is provided regarding datasets and its operations which are performed on it like training, validating of data and detection of attacks and beacon messages in communication. The authors in [8] Mohammad Asif Hossain and Wahidah Md Shah proposed they have considers the award and penalty scheme which is based on the case of student and teacher learning process. With this scheme it can reduce the network overhead and delay.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET

Kadam¹,

Sekhar²

2021

IJACSA

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Most of the self-driving vehicles are suspect of the of the different types attacks due to its communication pattern and changing network topology characteristics, these types of vehicles are dependent on external communication sources of VANET, which is a vehicular network, It has attracted great interest of industry and academia, but it is having a number of issues like security, traffic congestion, road safety which are not addressed properly in recent years. To address these issues it's required to build secure framework for the communication system in VANET and to detect different types of attack are the most important needs of the network security, which has been studied adequately by many researchers. However to improve the performance and to adapt the scenario of VANET, here in this paper we proposed a novel Hybrid KSVM scheme which is based on KNN and SVM algorithm to build a secure framework to detect Distributed Daniel of Service attack which is the part of Machine Learning approach. The experimental results shows that this approach gives the better results as compared to different Machine Learning based Algorithms to detect Distributed Daniel of Service attack.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET

Kadam¹,

Sekhar²

2021

IJACSA

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“…VANET security is a well-researched topic, with solutions ranging from the most common and traditional Public Key Infrastructure (PKI) [10][11][12][13][14] to others, such as IDbased or situation-modeling approaches [15]. However, the approach using IDSs, more precisely, intelligent IDSs, is more recent, with interest growing since 2010 [8]. In the Systematic Literature Review (SLR) performed in previous works [8], it was found that the most common approach type of solution used a hierarchical architecture, with 10 of the 19 research works choosing this approach.…”

Section: Related Workmentioning

confidence: 99%

“…However, the approach using IDSs, more precisely, intelligent IDSs, is more recent, with interest growing since 2010 [8]. In the Systematic Literature Review (SLR) performed in previous works [8], it was found that the most common approach type of solution used a hierarchical architecture, with 10 of the 19 research works choosing this approach.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, this is a key aspect of this work. Unfortunately, in the previously performed SLR [8], it was found that most of the research work in the literature fails to publish their datasets or the methodology used to produce them. Thus, the VANETs datasets that were made and publicly published in a prior work [32] were selected.…”

Section: Datasetsmentioning

confidence: 99%

“…As found in [8], most of the published research does not make the datasets available, making it difficult for third parties to verify the results. Thus, another contribution of this work is the suite of vehicular communications datasets, which has been made publicly available.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Hierarchical Security Framework for VANETs

2021

Self Cite

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Vehicular Ad hoc Networks (VANETs) are an emerging type of network that increasingly encompass a larger number of vehicles. They are the basic support for Intelligent Transportation Systems (ITS) and for establishing frameworks which enable communication among road entities and foster the development of new applications and services aimed at enhancing driving experience and increasing road safety. However, VANETs’ demanding characteristics make it difficult to implement security mechanisms, creating vulnerabilities easily explored by attackers. The main goal of this work is to propose an Intelligent Hierarchical Security Framework for VANET making use of Machine Learning (ML) algorithms to enhance attack detection, and to define methods for secure communications among entities, assuring strong authentication, privacy, and anonymity. The ML algorithms used in this framework have been trained and tested using vehicle communications datasets, which have been made publicly available, thus providing easily reproducible and verifiable results. The obtained results show that the proposed Intrusion Detection System (IDS) framework is able to detect attacks accurately, with a low False Positive Rate (FPR). Furthermore, results show that the framework can benefit from using different types of algorithms at different hierarchical levels, selecting light and fast processing algorithms in the lower levels, at the cost of accuracy, and using more precise, accurate, and complex algorithms in nodes higher in the hierarchy.

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A systematic literature review of intrusion detection systems in the cloud‐based IoT environments

Luo¹,

Chen²,

Mohammed

2022

Concurrency and Computation

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As the number of Internet of Things (IoT) subscribers, services, and applications grows, there is a pressing need for a reliable and lightweight security solution that can be used in IoT contexts. Also, due to the open nature of cloud computing, safety concerns are always challenging. One potential solution for this problem is an intrusion detection system (IDS). An ID that utilizes a machine learning method is gaining popularity since it has the benefit of automatically updating to fight against any new form of attack. Due to the importance of IDS in cloud-based IoT, the main articles and essential techniques in this domain are examined systematically. In cloud-based IoT, IDSs are categorized into three major categories, including learning-based, pattern-based, and rule-based mechanisms. The findings illustrate that the biggest challenge in IDS is precision and detection, which many researchers are trying to improve. Also, with the rise of connected objects, the most frequently utilized centralized (cloud-based) IDS struggles with excessive latency and network overhead, leading to delayed detection of unauthorized users and unresponsiveness to assaults. The results will be valuable for academicians, and they can offer visions for future research.

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A Systematic Review on Intelligent Intrusion Detection Systems for VANETs

Cited by 13 publications

References 29 publications

Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET

Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET

An Intelligent Hierarchical Security Framework for VANETs

A systematic literature review of intrusion detection systems in the cloud‐based IoT environments

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