Intrusion Detection System with an Ensemble Learning and Feature Selection Framework for IoT Networks

ABSTRACT

The Internet of Things (IoT) and its applications are currently the most popular research areas. The properties of IoT are easily adapted to real-life applications but they disclose threats. In computer security, the Intrusion Detection System (IDS) plays an essential role in identifying and repealing malicious deeds in computer networks. The main purpose of this work is motivated by IoT security enhancement for IDS development using ensemble learning and proposing suitable methods for classifier performance. Initially, the preprocessing strategy is used for data cleaning, encoding and normalization, which are conducted in the RPL-NIDDS17 dataset. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Secondly, the Convolution Neural Network (CNN) has been used to extract the features from the dataset. From the extracted features, the optimal features are selected by the proposed Arithmetic Optimization Algorithm (AOA). Finally, it is applied to the proposed weighted majority voting classifier. The AOA with the Butterfly Optimization Algorithm (BOA) is utilized to integrate the predictions of different classifiers to select the most vote class. This enhances the chances of perceived RF, kNN, SVM kernel, Bi-LSTM and GRU classifiers. The proposed method experiment is conducted in the MATLAB platform with the RPL-NIDDS17 dataset. The proposed scheme shows better performances in terms of accuracy, error, sensitivity, specificity, FPR, F1_score, Kappa and MCC, which are compared with the existing methods and algorithms.

KEYWORDS:

• Arithmetic Optimization Algorithm
• Butterfly Optimization Algorithm
• Ensemble classifier
• Internet of Things
• Intrusion detection system
• RPL-NIDDS17

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

G. Rohini

G Rohini received the BE degree in electronics and communication engineering from PSG College of Technology, Coimbatore in 1992. She received her ME degree in applied electronics from College of Engineering, Anna University, Chennai in 2002 and the PhD in VLSI design and testing, from College of Engineering, Guindy, Anna University, Chennai. She has twenty three years of teaching and four years of industrial experience. She is a corporate member of IETE, Member of IEEE, and Life Member of ISTE. Her research interests include VLSI design and design for testability, high speed low power VLSI architectures, algorithms for VLSI signal processing, image processing, IoT and embedded systems.

C. Gnana Kousalya

C Gnana Kousalya received her BE degree in electronics and communication engineering from the Government College of Technology, Coimbatore in 1993. She received her ME degree in applied electronics from Sathyabama University, Chennai in 2004 and the PhD in wireless sensor networks, from College of Engineering, Guindy, Anna University, Chennai. She has twenty four years of teaching and one year of industrial experience. She is a member of IEEE RAS, IEEE WIE & IEEE EMBS. She is also a corporate member of IETE and a life member of ISTE. Her research interests include security in wireless sensor networks, image processing, signal processing, IoT and embedded systems design. Email: [email protected]

J. Bino

J Bino completed his under graduate in electronics and communication engineering and post graduate in computer and communication from Anna University, Chennai. He is currently a part-time research scholar in the Department of Electronics Engineering, Madras Institute of Technology, Anna University, He is a member of IEEE BTS & IEEE SIGHT. His areas of interest are wireless networks, software defined radio and spectrum sensing in cognitive radio networks. Email: [email protected]