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Updated: May 3, 2026

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Intrusion detection in machine learning based E-shaped structure with algorithms, strategies and applications in

Suriyan Kannadhasan1, Ramalingam Nagarajan2

  • 1Department of Electronics and Communication Engineering, Study World College of Engineering, Tamilnadu, India.

Heliyon
|May 20, 2024
PubMed
Summary

A novel filtered deep learning model enhances intrusion detection in Internet of Things (IoT) networks, achieving 96.12% accuracy. This approach effectively identifies zero-day attacks in distributed sensor networks.

Keywords:
Accuracy and radiation patternE-Shape patch antennaIntrusionMachine learningReflection coefficientVSWRWSN

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Area of Science:

  • Computer Science
  • Network Security
  • Wireless Communication

Background:

  • Distributed sensor networks are crucial for modern applications like cloud computing and the Internet of Things (IoT).
  • Existing intrusion detection systems (IDS) struggle with anomaly detection and identifying zero-day attacks in diverse IoT data streams.
  • Current signature-based IDS are insufficient for the evolving threat landscape in IoT environments.

Purpose of the Study:

  • To introduce a novel Filtered Deep Learning Model for Intrusion Detection with a Data Communication Approach.
  • To enhance the detection of zero-day attacks in IoT networks by addressing challenges with varied data sources.
  • To improve the accuracy and efficiency of intrusion detection systems in distributed sensor networks.

Main Methods:

  • The proposed model involves five stages: Sensor Network Initialization, Cluster Formation and Head Selection, Connectivity, Attack Detection, and Data Broker.
  • A filtered deep learning approach is employed for anomaly detection within the sensor network.
  • An E-shaped patch antenna was designed, fabricated, and tested for wide-band wireless communication.

Main Results:

  • The Filtered Deep Learning Model achieved a superior accuracy of 96.12% in intrusion detection compared to existing Deep Learning Neural Net and Artificial Neural Network models.
  • Experimental results demonstrated the model's effectiveness in identifying intrusions in IoT networks.
  • The developed E-shaped patch antenna exhibited a wide bandwidth, resonating at 7.5 and 8.5 GHz, with detailed performance characteristics presented.

Conclusions:

  • The proposed filtered deep learning model significantly improves intrusion detection accuracy in IoT networks, outperforming traditional methods.
  • The study highlights the potential of advanced deep learning techniques for securing distributed sensor networks against sophisticated cyber threats.
  • The novel E-shaped patch antenna design offers a promising solution for efficient wide-band wireless communication in IoT applications.