Nature-Inspired Learning Framework for Cyberattack Classification in IoT Networks

Ishwarya K.^1,*, Saraswathı S.²

¹Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamilnadu, India

²Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India

Emails: ishwaryk3@srmist.edu.in; saraswathi.s.cse@sathyabama.ac.in

Abstract

Due to the massive data and communication progress, the usage of Internet of Things (IoT) devices has developed significantly. The extensive use of IoT systems heightens the complex interactions among devices and increases the data traffic, generating numerous possibilities for cyber challengers. Therefore, identifying and alleviating cyber-attacks focusing on IoT systems has appeared as an essential obligation in the context of cybersecurity. Academics and enterprises are contemplating means of machine learning (ML) and deep learning (DL) for cyberattack prevention because ML and DL exhibit great potential in numerous domains. Various DL teachings are executed to extract several patterns from multiple annotated datasets. DL is a beneficial tool for identifying cyberattacks. Timely network data detection and segregation become more fundamental than alleviating cyberattacks. Therefore, this paper proposes a novel Brown Bear Optimization method with an Ensemble of Machine Learning-based Cyber Attack Detections (BBOA-EMLCADs) method for secure IoT environment. The main aim of the BBOA-EMLCAD method relies on the automatic classification of the cyber threats in the IoT environment. Initially, the brown bear optimization (BBO) method is utilized for feature selection (FS). Moreover, an ensemble of two ML approaches namely XGBoost and least square support vector machine (LSSVM) are employed for the automatic identification of the cyber-attacks. Lastly, the salp swarm algorithms (SSAs) is implemented for the optimal hyperparameter tuning of the two ML techniques. The simulation validation of the BBOA-EMLCAD approach is performed under the WSN-DS dataset. The comparison assessment of the BBOA-EMLCAD approach portrayed a superior accuracy value of 99.62% over existing models.

Keywords: Cyber Attack Detection; Brown Bear Optimization; Machine Learning; Hyperparameter Tuning; Internet of Things