Detection of Malicious Nodes in Flying Ad-hoc Network with Supervised Machine Learning
subject: Linear Regression; Logistic Regression; Node Existence Matrix; Node Reputation Matrix; Path Reputation Matrix
description: An Ad-hoc network (FANET) is a new upcoming technology which has been used in several sectors. Ad-hoc networks are mostly wireless local area networks (LANs). The devices communicate with each other directly instead of relying on a base station or access points as in wireless LANs for data transfer. In an Ad-hoc network the communication between one node to another in a FANET is not secured and there isn't any authorized protocol for secured communication. Therefore, we suggest an algorithm to detect the malicious node in a network. This algorithm uses Linear regression to calculate the reputation or trust value of a node in the network. Then the above found trust value is used to classify the node as normal node or malicious node based on the Logistic Regression Classification. Thus, allowing a secure communication of data and avoiding attacks. 2022 IEEE.
creator: Tangade S.; Kumaar R.A.; Malavika S.; Monisha S.; Azam F.
source: Proceedings of the 3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-166545664-7
format: Online
language: English
type: Conference paper
coverage: Tangade S., CHRIST University, Dept. of CSE., Bengaluru, India; Kumaar R.A., School of ECE, REVA University, Bengaluru, India; Malavika S., School of ECE, REVA University, Bengaluru, India; Monisha S., School of ECE, REVA University, Bengaluru, India; Azam F., School of Comp. Science and Engineering, REVA University, Bengaluru, India