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Meena, C.
- Analysis of Machine Learning Classifiers to Detect Malicious Node in Vehicular Cloud Computing
Abstract Views :217 |
PDF Views:2
Authors
A. Sheela Rini
1,
C. Meena
1
Affiliations
1 Department of Computer Science, Avinashilingam Institute for Home Science & Higher Education for Women, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science, Avinashilingam Institute for Home Science & Higher Education for Women, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 9, No 2 (2022), Pagination: 202-213Abstract
VANET or Vehicular networks are created using the principles of MANETS and are used by intelligent transport systems to offer efficient communication between the domains of vehicles. Increasing the number of vehicles requires communication between vehicles to be fast and secure, where cloud computing with VANET is more prominent. To provide a secure VANET communication environment, this paper proposes a malicious or hacked vehicle identification system. Malicious vehicles are identified using four steps. The first step uses a clustering algorithm for similar group vehicles. In the Second step, cluster heads are identified and elected. In the next step, Multiple Point Relays are selected. Finally, classifiers are used to identify hacked vehicles. However, the existing system performance degrades as soon as the number of vehicles increases, resulting in increased cost during Cluster head election, inability to produce stable clusters, and the need for accurate and fast classification in high traffic scenarios. This work improves clustering algorithms and examines several classification algorithms to solve these issues. The classifiers analyzed are Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Naïve-Bayes (NB). A Hybrid classifier that combines SVM and KNN classifiers is also analyzed for its effectiveness to detect malicious vehicles. From the experimental results, it could be observed that the detection accuracy is high while using the hybrid classifier.Keywords
VANET, Malicious Node, SVM, Decision Tree, Naïve-Bayes, KNN.References
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- Dynamic Integration of Fast Furious Cheetah Optimization for Efficient and Secure Routing in Vehicular Ad Hoc Networks
Abstract Views :20 |
PDF Views:0
Authors
A. Sheela Rini
1,
C. Meena
1
Affiliations
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 11, No 2 (2024), Pagination: 248-273Abstract
This research addresses the intertwined challenges of routing efficiency and data security in Vehicular Ad Hoc Networks (VANETs), characterized by dynamic Vehicle-to- Vehicle (V2V) communication. To bolster the Ad Hoc On- Demand Distance Vector (AODV) protocol, Route Life Time Enhanced AODV (RLE-AODV) is introduced, integrating Fast Furious Cheetah Optimization (FFCO) at each protocol step for comprehensive optimization. The robust security measures are concurrently incorporated using an enhanced iteration of Elliptic Curve Cryptography (ECC), which is seamlessly integrated into the secure routing framework. The study meticulously explores the synergistic integration of FFCO with RLE-AODV and ECC, optimizing routing efficiency while fortifying data security. After integration with ECC, the framework transforms into Fast Furious Cheetah Optimization- Based Secured Routing (FFCOSR), ensuring the integrity and confidentiality of data exchanged between vehicles. Through extensive simulations, the FFCOSR framework demonstrates superior performance and heightened security compared to conventional approaches in V2V VANETs. By orchestrating FFCO within RLE-AODV, the approach dynamically adjusts routing parameters to adapt to changing network conditions, prolonging route stability and enhancing overall network performance. This research significantly advances state-of-theart efficient and secure vehicular communication, offering valuable insights into the synergy of optimization techniques for addressing multifaceted network challenges. The proposed FFCOSR framework represents a promising avenue for improving the reliability and security of V2V communication in VANETs, with potential applications in real-world scenarios where robustness and efficiency are paramount.Keywords
Hoc On-Demand Distance Vector Routing, Particle Swarm Optimization, Machine Learning, Network Lifespan, Energy Balancing, Localization, Clustering, Routing Overhead, Throughput, End-to-End Delay.References
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