Enhancing IoT Security: A Hybrid Deep Learning-Based Intrusion Detection System Utilizing LSTM, GRU, and Attention Mechanisms with Optimized Hyperparameter Tuning
محورهای موضوعی : Machine learning
Heshamt Asadi
1
,
Mahmood Alborzi
2
,
Hessam Zandhessami
3
1 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Intrusion Detection System in Internet of Things, Attention Mechanism in Deep Learning algorithm, Differential Evolution, Harmony Search,
چکیده مقاله :
Increasing complexity and volume of threats being created and targeted at cybersecurity for the IoTs necessitate the deployment of powerful IDSs. This paper offers an innovative intrusion detection system for IoTs networks based on deep learning. The new IDS employs the Long Short-Term Memory and Gated Recurrent Unit models’ strengths and an Attention Mechanism. First, the new IDS seeks to enhance the model’s ability to determine critical features in a vast amount of data streams and hence improve the ability to find potential cyber threats with high accuracy. The methodological framework used in a simulation and practical experiment setting was intended to recognize the unique nature of IoTs situations. therefore, used a hybrid algorithm optimization strategy, namely Differential Evolution and Harmony Search, to optimize the model due to the extensive hyperparameter space to get the best performance results. The results obtained superior accuracy, precision, recall, and F1 measures reaching 99.87 percent, 99.84 percent, 99.85 percent, and 99.85 percent is better than the performance measures achieved by existing models. Therefore, a deep learning-based hybrid IDS confirmed the research hypothesis that this could provide the necessary and effective cybersecurity for the IoTs. It is vital to note that this paper has contributed to the research topic by showing the potential of advanced neural architectures and strategic optimization tools to address the massive and sophisticated IoTs cybersecurity issues. Future research will be addressing whether these models can be applied in more IoTs settings and whether their real-time efficiency can be improved.
Increasing complexity and volume of threats being created and targeted at cybersecurity for the IoTs necessitate the deployment of powerful IDSs. This paper offers an innovative intrusion detection system for IoTs networks based on deep learning. The new IDS employs the Long Short-Term Memory and Gated Recurrent Unit models’ strengths and an Attention Mechanism. First, the new IDS seeks to enhance the model’s ability to determine critical features in a vast amount of data streams and hence improve the ability to find potential cyber threats with high accuracy. The methodological framework used in a simulation and practical experiment setting was intended to recognize the unique nature of IoTs situations. therefore, used a hybrid algorithm optimization strategy, namely Differential Evolution and Harmony Search, to optimize the model due to the extensive hyperparameter space to get the best performance results. The results obtained superior accuracy, precision, recall, and F1 measures reaching 99.87 percent, 99.84 percent, 99.85 percent, and 99.85 percent is better than the performance measures achieved by existing models. Therefore, a deep learning-based hybrid IDS confirmed the research hypothesis that this could provide the necessary and effective cybersecurity for the IoTs. It is vital to note that this paper has contributed to the research topic by showing the potential of advanced neural architectures and strategic optimization tools to address the massive and sophisticated IoTs cybersecurity issues. Future research will be addressing whether these models can be applied in more IoTs settings and whether their real-time efficiency can be improved.
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