Advanced CyberSecurity Solutions for IoT Based Networks
Keywords
Abstract
The proliferation of Internet of effects bias has introduced significant cybersecurity vulnerabilities compounded by their essential interconnectedness and resource limitations. This paper proposes a robust cybersecurity frame designed to guard IoT ecosystems. Our result integrates an Autoencoder for effective point birth and anomaly discovery Deep Neural Networks(DNNs) for sophisticated deep literacy- grounded attack bracket and Decision Trees for rapid-fire, interpretable real- time trouble identification. By assaying live data from IoT bias, the system effectively detects anomalies and directly classifies different cyber pitfalls including Denial of Service(DoS) attacks and unauthorized access attempts. This multi-layered approach leverages the Autoencoder's capability to learn normal data patterns and highlight diversions while DNNs use these uprooted features to fete intricate attack autographs with high perfection. The addition of Decision Trees ensures nippy and transparent bracket critical for nimble trouble response. This intertwined system significantly improves trouble discovery capabilities and accelerates response times thereby strengthening the overall security posture of IoT networks. The proposed result offers an adaptive and visionary defense against the dynamic and evolving diapason of cyber pitfalls in the expanding IoT geography which decreasingly includes criticalcyber-physical systems(CPS) like Industrial IoT(IIoT) bias within sectors similar as heads and mileage shops integral to the dependable operation of artificial control systems(ICS) including SCADA, DCS, PLCs, and Modbus protocols.
Copyright & License
Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this Article
Nammi Arun Kumar, Dr. G. Narasimha Rao (2025). Advanced CyberSecurity Solutions for IoT Based Networks. International Journal of Technology & Emerging Research (IJTER), 1(3), 240-248
BibTeX
@article{ijter2025212507282388,
author = {Nammi Arun Kumar and Dr. G. Narasimha Rao},
title = {Advanced CyberSecurity Solutions for IoT Based Networks},
journal = {International Journal of Technology & Emerging Research },
year = {2025},
volume = {1},
number = {3},
pages = {240-248},
issn = {3068-109X},
url = {https://www.ijter.org/article/212507282388/advanced-cybersecurity-solutions-for-iot-based-networks},
abstract = {The proliferation of Internet of effects bias has introduced significant cybersecurity vulnerabilities compounded by their essential interconnectedness and resource limitations. This paper proposes a robust cybersecurity frame designed to guard IoT ecosystems. Our result integrates an Autoencoder for effective point birth and anomaly discovery Deep Neural Networks(DNNs) for sophisticated deep literacy- grounded attack bracket and Decision Trees for rapid-fire, interpretable real- time trouble identification. By assaying live data from IoT bias, the system effectively detects anomalies and directly classifies different cyber pitfalls including Denial of Service(DoS) attacks and unauthorized access attempts. This multi-layered approach leverages the Autoencoder's capability to learn normal data patterns and highlight diversions while DNNs use these uprooted features to fete intricate attack autographs with high perfection. The addition of Decision Trees ensures nippy and transparent bracket critical for nimble trouble response. This intertwined system significantly improves trouble discovery capabilities and accelerates response times thereby strengthening the overall security posture of IoT networks. The proposed result offers an adaptive and visionary defense against the dynamic and evolving diapason of cyber pitfalls in the expanding IoT geography which decreasingly includes criticalcyber-physical systems(CPS) like Industrial IoT(IIoT) bias within sectors similar as heads and mileage shops integral to the dependable operation of artificial control systems(ICS) including SCADA, DCS, PLCs, and Modbus protocols.},
keywords = { Industrial Control Systems (ICS), Internet of Things (IoT), Cybersecurity, Anomaly Detection, AutoEncoder, Deep Neural Networks (DNN), Decision Tree Classifier, Principal Component Analysis (PCA), Machine Learning, Feature Extraction, Attack Classification, SWaT Dataset, Denial of Service (DoS), Malicious Command Injection, Supervised and Unsupervised Learning, Real-time Threat Detection, Cyber-Physical Systems (CPS), SCADA Systems, Modbus Protocols, and Critical Infrastructure Protection.},
month = {Jul},
}