Comprehensive Survey of Deep Learning-Based Intrusion Detection for IoT Wireless Sensor Networks – Volume 11 Issue 5 | Satish Dekka | IJET Journal 2025
Comprehensive Survey of Deep Learning-Based Intrusion Detection for IoT Wireless Sensor Networks – Volume 11 Issue 5 | Satish Dekka | IJET Journal 2025
Open Access • Peer Reviewed • High Citation & Impact Factor • ISSN: 2395-1303
IJET-V11I5P16 | Volume 11 Issue 5 | September – October 2025
Comprehensive Survey of Deep Learning-Based Intrusion Detection for Securing Routing in IoT Wireless Sensor Networks
Authors: SATISH DEKKA , Dr. PRASADU PEDDI , Dr. MANENDRA SAI DASARI
SATISH DEKKA — Research Scholar, Shri JJT University, Rajasthan.
Dr. PRASADU PEDDI — Guide, Shri JJT University, Rajasthan.
Dr. MANENDRA SAI DASARI — Co-Guide, Shri JJT University, Rajasthan.
Abstract
The proliferation of Internet of Things (IoT) Wireless Sensor Networks (WSNs) incritical sectors demands robust security solutions to counter complex routing attacks such as sinkhole, blackhole, and selective forwarding. Conventional detection methods often fall short inresource-constrained, dynamic IoT WSN environments. Deep Learning (DL) techniquesincluding Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), andAutoencoders have shown remarkable ability to autonomously detect and classifyroutingsecurity threats with high accuracy. ....
.To read more please visit : https://ijetjournal.org/deep-learning-intrusion-detection-iot-wsn-satish-dekka/
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