Optimized Ensemble Learning Models Based on Clustering and Hybrid Deep Learning for Wireless Intrusion Detection

Abstract

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature.

KEYWORDS:

• Ensemble learning
• Deep learning
• Clustering
• Intrusion detection
• Wireless networks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Perumal Pitchandi

Perumal Pitchandi received his MCA degree in computer applications, from Adaikalamatha College, Vallam, Thanjavur, India in 1996 and his ME degree in software engineering from Sri Ramakrishna Engineering College, Coimbatore, India in 2008. He completed his PhD at Anna University Chennai in 2014. He is currently working as a professor at the department of computer science and engineering, Sri Ramakrishna Engineering College, Coimbatore. He has published more than 50 publications in reputed journals and conferences. His areas of interest are data mining, data analytics and big data and networks.

M. Nivaashini

M Nivaashini received her PhD degree in information and communication engineering from Anna University, Chennai in 2022. She received her master’s degree with a gold medal in biometrics and cyber security from PSG College of Technology, Coimbatore in 2017 and a bachelor’s degree in computer science and engineering from Dr. Mahalingam College of Engineering & Technology, Pollachi in 2015. She has 2 years of experience in teaching and 4 years of experience in research. Her research interests are in artificial intelligence, the internet of things, big data analytics, and cyber security. She has around 33 publications in reputed journals and conferences. Email: [email protected]

R. Kingsy Grace

R Kingsy Grace received her PhD degree in information and communication engineering from Anna University, Chennai in 2016. She received her master’s degree in computer science and engineering from Karunya Institute of Technology, Coimbatore in 2005 and a bachelor’s degree in computer science and engineering from Noorul Islam College of Engineering, in 2015. He is currently working as an associate professor at the department of computer science and engineering, Sri Ramakrishna Engineering College, Coimbatore. Her research interests are in cloud computing and IoT, networks, big data analytics and machine learning, and air pollution monitoring and analysis. She has around 60 publications in reputed journals and conferences. Email: [email protected]