Bi-Level Intrusion Detection in IoT Networks Using Ensemble Method and A-GRU-RNN Classifier

Abstract

For minimizing security vulnerabilities and attacks, which affect Internet of Things (IoT) applications’ performance, it is essential to design an efficient authentication protocol owing to the rapid deployment of IoT. This paper proposes a bi-level Intrusion Detection (ID) in IoT using an ensemble and Arctan-based Gated Recurrent Unit-Recurrent Neural Network (A-GRU-RNN) classifier. This work mainly concentrates on both major and minor attacks. Allowing the trusted nodes to join the network is the other motive of this work. Here, the nodes are registered to the network and then initialized. Then, to verify the trust levels of the nodes, test packet transmission is performed. By employing the Deterministic Initialization Method-centric K-Means algorithm (DIM-K-Means) algorithm, the trusted nodes are formed into the cluster. Next, the respective CH is selected by Multiplex-Valued Encoding Sea Lion Optimization (MVE-SLO). Afterwards, by employing Multi-Point Relays-Optimized Link State Routing (MPR-OLSR), routing is taken place. The steps, namely preprocessing, attribute extraction, attribute reduction, and classification are taken to detect the confidentiality of the sensed data. The classification phase significantly determines whether the data is attacked or not. If the data is attacked, then it detects the attack type. Here, the publicly available dataset is used. As per the experimental outcome, the proposed method withstands high-security levels when analogized to the prevailing methodologies.

Keywords:

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Gaussian Discriminant Analysis (GDA)
• Recurrent Neural Network (RNN)
• K-means

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

Geo Francis E.

Geo Francis E. received the B.Sc. degree in Computer Science, Mathematics and Statistics from Bangalore University, India in 2004 and the MCA degree from Madras University, India in 2007. He has also received the B.Ph. degree in Philosophy and the B.Th. degree in Theology from Dharmaram Vidya Kshetram, a Pontifical Athenaeum, Bangalore in 2001 and 2010, respectively. He is currently pursuing the Ph.D. degree in Computer Science with Karpagam Academy of Higher Education, Coimbatore, India. He is a Junior Higher Secondary School Teacher in Computer Science at St. Aloysius H.S.S, Elthuruth, Kerala, India. His research interests include Network Security, Cryptography, IoT and Machine Learning.

Sheeja S.

Sheeja S. is a Professor in Computer Science at Karpagam Academy of Higher Education (Deemed to be University) at Coimbatore, Tamil Nadu, India, since 2005. She holds Ph.D. Degree in Computer Science from Bharathiar University. She has 20 years of teaching experience. Her areas of interest include Computer Network, IOT, Mobile Adhoc Network, Wireless Sensor Network, Image Processing, Web Services. She has published several articles in various international and national journals and presented conference papers in national and international level. She has 2 patents to her credit. She has a good number of citations for her publications.