Design and Implementation of a Hybrid Deep Learning Algorithm for IoT Attack Detection Using Harmony Search for Hyperparameter Optimization and Feature Selection

Authors

    Zahra Bakhshali PhD student, Department of Information Technology Management, Science and Research Unit, Islamic Azad University, Tehran, Iran.
    Alireza Poorebrahimi * Assistant Professor, Department of Industrial Management, Karaj Branch, Islamic Azad University, Alborz, Iran. poorebrahimi@gmail.com
    Ahmad Ebrahimi Assistant Professor, Department of Industrial Management and Technology, Science and Research Unit, Islamic Azad University, Tehran, Iran.
    Nazanin Pilehvari Professor, Department of Industrial Management, West Tehran Branch, Islamic Azad University, Tehran, Iran.

Keywords:

Harmony search algorithm, Neural Networks, CNN-LSTM model, feature selection

Abstract

Objective: The primary objective of this study is to design and implement a hybrid deep learning algorithm that integrates CNN and LSTM architectures for the proactive detection of attacks in IoT networks. Methodology: This study develops a hybrid CNN-LSTM algorithm for IoT attack detection, utilizing Harmony Search for both hyperparameter optimization and feature selection. The Harmony Search algorithm is applied in two optimization stages: first for tuning hyperparameters, and second for selecting relevant features. The hybrid model is trained on IoT data and evaluated based on metrics such as accuracy, precision, recall, and F1 score. Findings: The results indicate that the CNN-LSTM model optimized with Harmony Search demonstrates superior accuracy and efficiency in detecting IoT attacks compared to traditional methods, leading to improved key performance indicators and reduced computational overhead. Conclusion: This approach can serve as a robust solution for proactive IoT attack detection and other security challenges. Objective: The primary objective of this study is to design and implement a hybrid deep learning algorithm that integrates CNN and LSTM architectures for the proactive detection of attacks in IoT networks. Methodology: This study develops a hybrid CNN-LSTM algorithm for IoT attack detection, utilizing Harmony Search for both hyperparameter optimization and feature selection. The Harmony Search algorithm is applied in two optimization stages: first for tuning hyperparameters, and second for selecting relevant features. The hybrid model is trained on IoT data and evaluated based on metrics such as accuracy, precision, recall, and F1 score. Findings: The results indicate that the CNN-LSTM model optimized with Harmony Search demonstrates superior accuracy and efficiency in detecting IoT attacks compared to traditional methods, leading to improved key performance indicators and reduced computational overhead. Conclusion: This approach can serve as a robust solution for proactive IoT attack detection and other security challenges.

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Published

2022-06-21

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مقالات

How to Cite

Bakhshali, Z., Poorebrahimi, A., Ebrahimi, A., & Pilehvari, N. (2022). Design and Implementation of a Hybrid Deep Learning Algorithm for IoT Attack Detection Using Harmony Search for Hyperparameter Optimization and Feature Selection. Dynamic Management and Business Analysis, 3(4), 176-190. https://dmbaj.org/index.php/dmba/article/view/144

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