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Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models
Shaza Dawood Ahmed Rihan1, Mohammed Anbar2, Basim Ahmad Alabsi1
1Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 9, 2023
Summary
This study introduces a novel approach for detecting Internet of Things (IoT) attacks by combining ensemble feature selection with deep learning models. The method significantly enhances the accuracy and reliability of IoT security systems.
Area of Science:
- Cybersecurity
- Machine Learning
- Network Security
Background:
- The proliferation of Internet of Things (IoT) devices has led to increased cybersecurity vulnerabilities.
- IoT attacks pose significant threats to both organizations and individuals, necessitating advanced detection methods.
Purpose of the Study:
- To propose and evaluate an effective approach for detecting attacks in IoT networks.
- To assess the performance of deep learning models enhanced by ensemble feature selection.
Main Methods:
- Utilized ensemble feature selection, combining filter methods (variance threshold, mutual information, Chi-square, ANOVA, L1) with Recursive Feature Elimination (RFE).
- Evaluated the impact of selected features on various Deep Learning (DL) models, including CNN, RNN, GRU, and LSTM.
- Tested the proposed method on the IoT-Botnet 2020 dataset, measuring detection accuracy, precision, recall, F1-measure, and False Positive Rate (FPR).
Main Results:
- All evaluated DL models demonstrated high performance, achieving detection accuracy between 97.05% and 97.87%.
- Precision, recall, and F1-measure values were also notably high, ranging from 96.99% to 97.95%, 99.80% to 99.95%, and 98.45% to 98.87%, respectively.
- The refined feature set derived from ensemble selection significantly improved DL model performance in IoT attack detection.
Conclusions:
- The proposed ensemble feature selection combined with DL models offers a robust solution for detecting IoT network attacks.
- The findings highlight the effectiveness of this hybrid approach in improving cybersecurity for IoT environments.
- This research contributes to developing more secure and reliable IoT ecosystems through advanced threat detection.
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