RETRACTED ARTICLE: A stream position performance analysis model based on DDoS attack detection for cluster-based routing in VANET

Kolandaisamy, Raenu; Noor, Rafidah Md; Kolandaisamy, Indraah; Ahmedy, Ismail; Kiah, Miss Laiha Mat; Tamil, Mohd Emran Mohd; Nandy, Tarak

doi:10.1007/s12652-020-02279-2

Cited by 37 publications

(16 citation statements)

References 21 publications

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“…A DDOS attack is similar to a DOS attack except that the DDOS involves more than one adversary attacking from different locations. The adversaries engulf the IoD network with unnecessary messages to make it unworkable [113].…”

Section: Distributed Denial Of Service (Ddos) Attackmentioning

confidence: 99%

Internet of Drones Security and Privacy Issues: Taxonomy and Open Challenges

et al. 2021

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Internet of Drones (IoD) is a decentralized network and management framework that links drones' access to the controlled airspace and provides inter-location navigation services. The interconnection of drones in the IoD network is through the Internet of Things (IoT). Hence the IoD network is vulnerable to all the security and privacy threats that affect IoT networks. It is highly required to safeguard a good atmosphere free from security and privacy threats to get the desired performance from IoD applications. Security and privacy issues have significantly restricted the overall influence of the IoD paradigm. There are existing survey studies that helped lay a vital foundation for understanding the IoD security and privacy issues. However, not all have thoroughly investigated the level of security and privacy threats associated with the various drone categories. Besides, most existing review studies do not examine secured IoD architecture. This paper aims to assess the recent trends in the security and privacy issues that affect the IoD network. We investigate the level of security and privacy threats of the various drone categories. We then highlight the need for secured IoD architecture and propose one. We also give a comprehensive taxonomy of the attacks on the IoD network. Moreover, we review the recent IoD attack mitigating techniques. We also provide the performance evaluation methods and the performance metrics employed by the techniques. Finally, we give research future direction to help researchers identify the latest opportunities in IoD research.

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Section: Distributed Denial Of Service (Ddos) Attackmentioning

confidence: 99%

Internet of Drones Security and Privacy Issues: Taxonomy and Open Challenges

et al. 2021

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“…Kolandaisamy et al 19 proposed using an analytical approach that utilizes the measures gathered by packet marking based on an adapted stream region scheme. The proposed approach involves extracting the neighbor log file, calculating the node's value, deciding on the source region, identifying the routes for each region, and computing the circulation rate.…”

Section: Statistical-based Studies For Ddos Attackmentioning

confidence: 99%

“…The obtained classes as shown in Figure 2 are as follows: (1) Category 1: studies that used statistical methods rather than ML techniques for detecting attacks on networks. 13–20 (2) Category 2: studies that used ML techniques trained on datasets of VANET to evaluate detection of network attacks including DDoS attacks. 21–25 (3) Category 3: studies satisfying the filtering criteria for Category 2, but not including DDoS attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

Alhaidari

Alrehan

2021

International Journal of Distributed Sensor Networks

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Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

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“…Another approach to detect and locate DDoS attacks in VANET is Stream Position Performance Analysis (SPPA) proposed in [8,23]. By calculating and monitoring several metrics, such as conflict field, conflict data, and attack signature sample rate (CCA), this approach inspects the position of any field station in sending the information to launch a DDoS attack.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Using the calculated metrics, CCA distinguishes malicious and trustworthiness packets. Its DDoS detection performance was reported to be remarkable [8,23].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

Al-Mehdhara

Ruan

2021

Wireless Communications and Mobile Computing

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The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial-of-service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software-Defined Networking (SDN) controller and Neural Network Self-Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent capabilities to the VANET. To resolve the drawbacks of standard SOMs and to enhance the SOM’s efficiency, a Multilayer Distributed SOM (MSOM) model based on two levels of clustering and classification is used. Experimental results show that our solution can efficiently detect malicious traffic, prevent and mitigate DDoS attacks, and increase system security and recovery speed from the attacking traffic. Moreover, the proposed scheme achieves a high accuracy rate (99.67%). Simulation results demonstrate the effectiveness and efficiency of the MSOM regarding detection accuracy and other studied metrics.

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RETRACTED ARTICLE: A stream position performance analysis model based on DDoS attack detection for cluster-based routing in VANET

Cited by 37 publications

References 21 publications

Internet of Drones Security and Privacy Issues: Taxonomy and Open Challenges

Internet of Drones Security and Privacy Issues: Taxonomy and Open Challenges

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

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