Designing a Hybrid Algorithm that Combines Deep Learning and PSO for Proactive Detection of Attacks in IoT Networks
محورهای موضوعی : Machine learning
Zahra Bakhshali
1
,
Alireza Pourebrahimi
2
*
,
Ahmad Ebrahimi
3
,
Nazanin Pilevari
4
1 - Department of Information Technology Management, SRC, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, Karaj Branch, Islamic Azad University, Alborz, Iran
3 - Department of Industrial and Technology Management, SRC, Islamic Azad University, Tehran, Iran
4 - Department of Industrial Management, West Tehran Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Deep Learning Algorithms, Internet of Things, IoT Attacks, PSO algorithm,
چکیده مقاله :
As a result, with the establishment of Internet of Things (IoT) at a booming pace, the demand for effective, green security systems to detect cyber-attacks is escalating. Despite thorough investigation in this domain, the heterogeneous nature and multifaceted characteristic of IoT data make successful attack detection a challenging task. This paper introduces a new method for enhancing IoT attack detection through a hybrid deep learning model (CNN-GRU-LSTM) integrated with Particle Swarm Optimization (PSO) for hyperparameter optimization. This methodology consists of different steps, starting with a CSV (Comma Separated Values) file to use it as the dataset, performing different data science operations like feature selection, calculating weights to balance the class for learning the model, etc. A hybrid CNN-GRU-LSTM model is subsequently established and trained with the integration of the merit of each algorithm: CNN for spatial feature abstraction, GRU for effectiveness in managing the sequential information, and LSTM for discovering the long-range dependencies. The hyperparameters of the PSO algorithm are optimized to find the best combination of features/parameters to improve detection performance and efficiency. The results show remarkable accuracy and efficiency improvements over traditional methods. H. PSO for Optimizing Hybrid Deep Learning Architecture The gainful approach to building deep neural networks for IoT frameworks is through PSO based improvements. The results help to advance a realm of research work in IoT security and lay a grouped foundation for further work in optimizing attack detection models with different machine learning algorithms and optimization approaches.
As a result, with the establishment of Internet of Things (IoT) at a booming pace, the demand for effective, green security systems to detect cyber-attacks is escalating. Despite thorough investigation in this domain, the heterogeneous nature and multifaceted characteristic of IoT data make successful attack detection a challenging task. This paper introduces a new method for enhancing IoT attack detection through a hybrid deep learning model (CNN-GRU-LSTM) integrated with Particle Swarm Optimization (PSO) for hyperparameter optimization. This methodology consists of different steps, starting with a CSV (Comma Separated Values) file to use it as the dataset, performing different data science operations like feature selection, calculating weights to balance the class for learning the model, etc. A hybrid CNN-GRU-LSTM model is subsequently established and trained with the integration of the merit of each algorithm: CNN for spatial feature abstraction, GRU for effectiveness in managing the sequential information, and LSTM for discovering the long-range dependencies. The hyperparameters of the PSO algorithm are optimized to find the best combination of features/parameters to improve detection performance and efficiency. The results show remarkable accuracy and efficiency improvements over traditional methods. H. PSO for Optimizing Hybrid Deep Learning Architecture The gainful approach to building deep neural networks for IoT frameworks is through PSO based improvements. The results help to advance a realm of research work in IoT security and lay a grouped foundation for further work in optimizing attack detection models with different machine learning algorithms and optimization approaches.
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