SDNStat-Sec: A Statistical Defense Mechanism Against DDoS Attacks in SDN-Based VANET

Bensalah, Faycal; Elkamoun, Najib; Baddi, Youssef

doi:10.1007/978-981-15-6048-4_46

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Finally, the identification of a DDoS attack is based on the deviation of the circulation from the current rate. Similarly, Bensalah et al 20 proposed a statistical method for detecting and controlling malicious nodes in a VANET using a variable control chart as a model to monitor the quality of the communication for each node. Then, based on the taken measurements, a node is considered a malicious node when its statistical quality violates the control limit.…”

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%

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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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Section: Statistical-based Studies For Ddos Attackmentioning

confidence: 99%

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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“…Security in VANETs is profoundly influenced by a spectrum of attacks that can significantly impede the performance of various routing protocols [ 21 , 22 , 23 ]. These attacks encompass diverse categories and are pivotal to elucidating fundamental forms of routing attacks, as delineated in Figure 3 .…”

Section: Introductionmentioning

confidence: 99%

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

ul Hassan,

Al-Awady,

Ali

et al. 2024

Sensors

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A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

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“…Many approaches have been applied to mitigate DDoS attacks including entropy mechanism ( [8], [9], [10] and [11]), blockchain method ( [12], [13], [14] and [15]), machine learning approach ( [16], [17], [18] and [19]), statistical technique ( [20], [21] and [22]) and epidemic approach [23], [24], [25], [26], [27], [28], [29] and [30]). Though, each of these approaches has contributed to some extents to the mitigation of DDoS attacks in networks.…”

Section: Introductionmentioning

confidence: 99%

VIQS Epidemic Modeling of Distributed Denial-of-Service Attacks in Software-Defined Networking

BIAOU,

OLUWATOPE,

OGUNDARE

et al. 2023

Preprint

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Software-Defined Networking (SDN) in view of securing a future for networking has been regularly facing a lot of treats and attacks. In this study, a VIQS (Vulnerable, Infected, Quarantined and Secured) epidemic is modelled and the mathematical analysis of the model is formulated and implemented to mitigate the Distributed Denial of Service (DDoS) attack in SDN. The proposed model is in an open population of nodes where the nodes have the liberty of movement from the point of view of the SDN architecture. This achieves the local and global stabilities of the free equilibrium when Ro < 1 and the endemic equilibrium when Ro > 1 of the DDoS attack in SDN. Therefore, the stability of the system has led to the extent that there are no longer infectious or quarantined nodes over time for both the local stability of attack free equilibria when Ro < 1 and the local stability of endemic equilibria when Ro > 1. It is proven that, with the consideration of the quarantine state transition of nodes in the proposed VIQS epidemic model built in an open population of nodes, the DDoS attack is carefully mitigated and under a total control.

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SDNStat-Sec: A Statistical Defense Mechanism Against DDoS Attacks in SDN-Based VANET

Cited by 5 publications

References 8 publications

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

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

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

VIQS Epidemic Modeling of Distributed Denial-of-Service Attacks in Software-Defined Networking

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