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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and

Abdulaziz Fatani^1,2, Abdelghani Dahou³, Mohamed Abd Elaziz^4,5,6,7

¹School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

This study introduces an advanced intrusion detection system (IDS) model combining deep learning and optimization. The novel method enhances network security by accurately identifying unknown cyber threats in cloud and IoT environments.

Keywords:

CNNs Growth Optimizer Internet of Things (IoT)cyber security intrusion detection system metaheuristics

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Area of Science:

Cybersecurity
Artificial Intelligence
Network Security

Background:

Intrusion detection systems (IDS) are vital for network security.
Traditional IDS struggle with novel and sophisticated cyber threats.
Metaheuristic algorithms and deep learning offer potential improvements for IDS.

Purpose of the Study:

To propose a novel IDS model integrating deep learning and optimization techniques.
To enhance the accuracy and efficiency of intrusion detection.
To effectively identify previously unknown cyber attacks.

Main Methods:

Feature extraction using Convolutional Neural Networks (CNNs).
A new feature selection method, Modified Growth Optimizer (MGO), incorporating the Whale Optimization Algorithm (WOA).
Evaluation on public cloud and Internet of Things (IoT) datasets.

Main Results:

The proposed IDS model demonstrated high accuracy in detecting intrusions.
The MGO feature selection method outperformed existing approaches.
The system effectively identified previously unknown attacks.

Conclusions:

The combined deep learning and optimization approach significantly improves IDS performance.
The MGO-based IDS is a promising solution for modern cybersecurity challenges.
The method shows strong potential for securing cloud and IoT networks against advanced threats.