Sensors, Vol. 23, Pages 8224: A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs

Sensors, Vol. 23, Pages 8224: A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs Sensors doi: 10.3390/s23198224 Authors: Amalia Amalia Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An 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&am...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research