A Multiclass Attack Classification Framework for IoT Using Hybrid Deep Learning Model

Saraladeve .L^1,*, Chandrasekar .A², Nithya .T³, Mohamed Imtiaz .N⁴, Kalaiarasi .S⁵, Balaji Sampathkumar⁶, Rajendran Thanikachalam⁷, Maria Arockia Dass .J³

¹Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

²Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai, Tamil Nadu, India

³Department of Computer Science and Business Systems, Rajalakshmi Institute of Technology (Autonomous), Chennai, Tamil Nadu, India

⁴Department of Information Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, Karnataka, India

⁵Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

⁶Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India

⁷Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

Emails: saraladeve@gmail.com; dean@stjosephs.ac.in; nithya.t@ritchennai.edu.in;  imtiaz4687@gmail.com; kalaiarasis.sse@saveetha.com;  bbaallaajjii@gmail.com; tlrajen@gmail.com;  dasscse2008@gmail.com

Abstract

In recent years, the Internet of Things (IoT) has emerged as one of the most significant concepts in numerous facets of our contemporary way of life. Nonetheless, addressing the concerns over the IoT's security presents the most significant obstacle to the widespread adoption of this technology. Using an Intrusion Detection System (IDS) to detect malicious activity in the networks is one of the most essential things that can be done to solve the security concerns posed by the IoT. Hence, a Deep Learning-based IDS (DL-IDS) model is designed for the multi-class classification of attacks in the IoT networks. This DL-IDS model includes data preprocessing, feature extraction, feature selection, and classification processes. The Bot-IoT and IoT-23 datasets are used as input for the research model. In preprocessing, the datasets are normalized, and the missing data are replaced. After preprocessing, the features are extracted using the Convolutional Neural Network (CNN) architecture. The features selection process is performed from the extracted features by implementing the Quantum-based Chameleon Swarm Optimization (QCSO) algorithm, which selects features from the datasets. Based on these features selected, the multi-class classification is carried out using the Deep Belief Network (DBN) for each attack presented in the datasets. The classification performance is performed individually for both datasets and evaluated using accuracy, detection rate, precision, and f1-scores. The performances of the proposed DL-IDS model are compared with the other models analyzed from the literature survey discussed in this work. The average scores obtained using the IoT-23 data set include 99.45% accuracy, 99.47% detection rate, 99.66% f1-scores, and 99.85% precision. For the Bot-IoT data, the average scores are 99.49% accuracy, 99.52% detection rate, 99.70% f1-score, and 99.88% precision.

Keywords: IoT; IDS; Features Selection; Multi-class Classification; CNN; QCSO; DBN