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CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things

Basim Ahmad Alabsi¹, Mohammed Anbar², Shaza Dawood Ahmed Rihan¹

¹Applied College, Najran University, Kind Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel CNN-CNN approach for detecting Internet of Things (IoT) attacks. The method effectively identifies network threats with high accuracy, enhancing IoT security.

Keywords:

Internet of Things IoT attacks convolutional neural network feature selection intrusion detection system

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Design and Analysis for Fall Detection System Simplification

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Area of Science:

Cybersecurity
Network Security
Artificial Intelligence

Background:

The proliferation of Internet of Things (IoT) devices has expanded global connectivity.
Increased IoT device usage elevates network vulnerabilities to diverse cyber threats.

Purpose of the Study:

To develop and evaluate a robust attack detection system for IoT networks.
To enhance the security and integrity of IoT ecosystems through advanced threat identification.

Main Methods:

A hybrid deep learning model combining two Convolutional Neural Networks (CNN-CNN) was proposed.
The first CNN model extracts significant features from network traffic data.
The second CNN model utilizes these features to construct an accurate attack detection system.

Main Results:

The CNN-CNN approach achieved 98.04% detection accuracy on the BoT IoT 2020 dataset.
High precision (98.09%) and recall (99.85%) were recorded, with a low false positive rate (1.93%).
The proposed method demonstrated superior performance compared to existing deep learning algorithms and feature selection techniques.

Conclusions:

The developed CNN-CNN model offers a highly effective solution for detecting attacks in IoT environments.
This approach significantly improves upon current methods for identifying and mitigating IoT network threats.
The findings underscore the potential of deep learning for securing the expanding landscape of connected devices.