A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment.

Journal: Scientific reports
Published Date:

Abstract

Internet of Health Things (IoHT) plays a vital role in everyday routine by giving electronic healthcare services and the ability to improve patient care quality. IoHT applications and devices become widely susceptible to cyber-attacks as the tools are smaller and varied. Additionally, it is of dual significance once IoHT contains tools applied in the healthcare field. In the context of smart cities, IoHT enables proactive health management, remote diagnostics, and continuous patient monitoring. Therefore, it is essential to advance a strong cyber-attack detection method in the IoHT environments to mitigate security risks and prevent devices from being vulnerable to cyber-attacks. So, improving an intrusion detection system (IDS) for attack identification and detection using the IoHT method is fundamentally necessary. Deep learning (DL) has recently been applied in attack detection because it can remove and learn deeper features of known attacks and identify unknown attacks by analyzing network traffic for anomalous patterns. This study presents a Securing Attack Detection through Deep Belief Networks and an Advanced Metaheuristic Optimization Algorithm (SADDBN-AMOA) model in smart city-based IoHT networks. The main aim of the SADDBN-AMOA technique is to provide a resilient attack detection method in the IoHT environment of smart cities to mitigate security threats. The data pre-processing phase applies the Z-score normalization method for converting input data into a structured pattern. For the selection of the feature process, the proposed SADDBN-AMOA model designs a slime mould optimization (SMO) model to select the most related features from the data. Followed by the deep belief network (DBN) method is used for the attack classification method. Finally, the improved Harris Hawk optimization (IHHO) approach fine-tunes the hyperparameter values of the DBN method, leading to superior classification performances. The effectiveness of the SADDBN-AMOA method is investigated under the IoT healthcare security dataset. The experimental validation of the SADDBN-AMOA method illustrated a superior accuracy value of 98.71% over existing models.

Authors

  • S Jayanthi
    Department of Artificial Intelligence & Data Science, Faculty of Science and Technology (IcfaiTech), The ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana, 01 503, India.
  • Sodagudi Suhasini
    Department of Information Technology, Siddhartha Academy of Higher Education, (Deemed to Be University), Kanuru, Vijayawada-07, AP, India.
  • N Sharmili
    Computer Science and Engineering Department, Gayatri Vidya Parishad College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
  • E Laxmi Lydia
    Department of Computer Science and Engineering, Vignan's Institute of Engineering for Women, Visakhapatnam, 530046, India.
  • V Shwetha
    Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Manipal, Karnataka, 576104, India. shwetha.v@manipal.edu.
  • Bibhuti Bhusan Dash
    School of Computer Applications, KIIT Deemed to be University, Bhubaneswar, India.
  • Mrinal Bachute
    Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), SIU, Lavale, Pune, Maharashtra, India.