@Article{iasc.2023.035276,
AUTHOR = {Zainab Ali Abbood, Doğu Çağdaş Atilla, Çağatay Aydin},
TITLE = {Intrusion Detection System Through Deep Learning in Routing MANET Networks},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {1},
PAGES = {269--281},
URL = {http://www.techscience.com/iasc/v37n1/52635},
ISSN = {2326-005X},
ABSTRACT = {Deep learning (DL) is a subdivision of machine learning (ML) that
employs numerous algorithms, each of which provides various explanations of
the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to MANET’s potential to provide
small-cost solutions for real-world contact challenges, decentralized management,
and restricted bandwidth, MANETs are more vulnerable to security threats. When
protecting MANETs from attack, encryption and authentication schemes have
their limits. However, deep learning (DL) approaches in intrusion detection systems (IDS) can adapt to the changing environment of MANETs and allow a system to make intrusion decisions while learning about its mobility in the
environment. IDSs are a secondary defiance system for mobile ad-hoc networks
vs. attacks since they monitor network traffic and report anything unusual.
Recently, many scientists have employed deep neural networks (DNNs) to
address intrusion detection concerns. This paper used MANET to recognize complex patterns by focusing on security standards through efficiency determination
and identifying malicious nodes, and mitigating network attacks using the three
algorithms presented Cascading Back Propagation Neural Network (CBPNN),
Feedforward-Neural-Network (FNN), and Cascading-Back-Propagation-NeuralNetwork (CBPNN) (FFNN). In addition to Convolutional-Neural-Network
(CNN), these primary forms of deep neural network (DNN) building designs
are widely used to improve the performance of intrusion detection systems
(IDS) and the use of IDS in conjunction with machine learning (ML). Furthermore, machine learning (ML) techniques than their statistical and logical methods
provide MANET network learning capabilities and encourage adaptation to different environments. Compared with another current model, The proposed model
has better average receiving packet (ARP) and end-to-end (E2E) performance.
The results have been obtained from CBP, FFNN and CNN 74%, 82% and
85%, respectively, by the time (27, 18, and 17 s).},
DOI = {10.32604/iasc.2023.035276}
}