Mobile ad-hoc networks (MANETS) are one of the emerging fields that have seamless applications in the field of emergency situations like rescue operations, commercial applications like virtual classrooms, medical like disease diagnosis etc. In the era of driverless vehicles, mobile ad-hoc network (MANET) finds a useful place in discoveries. However, MANETS are vulnerable to a number of attacks because of properties like non-existing infrastructure, dynamic topology, multihop network etc. A lot of previous works have focused on the impact of various attacks on routing protocols like jellyfish attacks, blackhole attacks or selfish node attacks. The area becomes more vulnerable to attacks as a network is in use for a limited short time as the topology is highly dynamic and time-specific. However, the use of machine learning and deep learning algorithms has given a new edge to MANETS in driverless vehicles. In literature, malicious node/ selfish node detection (passage of wrong information/ blockage of information) was done by using various supervised or unsupervised machine learning algorithms like KNN, SVM, CNN or recurrent networks. This paper presents the detection of various faulty nodes using the NS2 simulator and using machine learning algorithms. The study concluded that the application of machine learning and deep learning algorithms has increased the faulty node detection accuracy as compared to simulated code. The increased accuracy may be helpful in the formation of short-term and highly dynamic MANETS like driverless cars or military networks. In future, these networks may be connected with satellite images so as to forecast whether information like various DANA in Orissa, and SARA in US pandemics and take precautionary actions accordingly.
Author
(s) Details
Bhawna Singla
Geeta University, India.
A. K. Verma
Thapar University, India.
Please see the book here:- https://doi.org/10.9734/bpi/stda/v2/3679
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