Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC

Velayudhan, Nitha C.; Anitha, A.; Madanan, Mukesh

doi:10.1007/s12652-021-03379-3

Cited by 15 publications

(5 citation statements)

References 29 publications

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“…Using the elephant herding algorithm (EHA) with deep learning for intrusion detection in VANET, Velayudhan et al 14 identified the Sybil attack. Initially, cluster formation (CF), cluster head (CH) selection, and, finally, attack detection are included in the suggested technique.…”

Section: Related Workmentioning

confidence: 99%

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

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Section: Related Workmentioning

confidence: 99%

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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“…This method prevented the modification of classified events, but it did not include the large amounts of data obtained via vehicular communication to improve the SVM's performance. In [28], to improve security, the authors proposed the CMEHA-DNN deep learning-based intrusion detection model for the purpose of detecting Sybil attacks in VANETs. The Sybil attack is successfully identified by this method.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The Sybil attack is successfully identified by this method. These works [26][27][28] did not focus on blackhole attacks in VANETs. However, from the above works, we can conclude that machine learning techniques, especially DL, can be used to avoid attacks in VANETs.…”

Section: Related Work and Motivationmentioning

confidence: 99%

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs

Amalia,

Pramitarini,

Perdana

et al. 2023

Sensors

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Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node’s remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

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“…In references [42,43], the deep learning-centered intrusion detection system (IDS) and hybrid machine learning are developed for detecting the malicious attacks in VANET. e modified K-harmonic means-based clustering was developed in the deep learning-based IDS to reduce the propagation delay.…”

Section: Literature Reviewmentioning

confidence: 99%

BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

Ahmed

Frikha

Rahebi

2022

Mobile Information Systems

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This article proposes an ambulance vehicle routing approach in smart cities. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). It aims to take transfer the patients confidentially, accurately, and quickly. The type of CNN used in this research is a residual network (ResNet). The node method is responsible for creating the city map. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The driver feeds the data that contain the ambulance vehicle’s node position and the accident location to the BA-CNN vehicle routing algorithm. The algorithm then obtains the shortest path to reach the location of the accident by the driver. When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. The offline data are then trained by utilizing a neural network. The neural network is used for finding the shortest routes between source and destination. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV.

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Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC

Cited by 15 publications

References 29 publications

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs

BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

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