Secure Honeynet Cloud IoT Model and Machine Learning based Smart Healthcare System with Urban Management
S. Pavithra1,*, Venkatesan S.2, Yerragudipadu subbarayudu3, Keshav Sinha4, Rayavarapu Sridivya5, Munugapati Bhavana6
1Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India
2Assistant Professor, Department of Artificial Intelligence and Data Science, St. Joseph's Institute of Technology, Chennai, India
3Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad-500043, Telangana, India
4School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
5Senior Assistant Professor, Department of CSE-AIML, ADITYA UNIVERSITY, Hyderabad, India
6Assistant Professor, Department of Computer Science and Engineering, MLR INSTITUTE OF TECHNOLOGY, DUNDIGAL, HYDERABAD, India
Emails: pavithrs9@srmist.edu.in; venkadesh58@gmail.com; subbarayudu.kluh@gmail.com; Keshav.sinha@yandex.com; rayavarapudivya@gmail.com; vbhaavana369@gmail.com
Abstract
Smart health is becoming an increasingly sensitive field because to the growing use of a variety of Internet of Medical Things (IoMT) devices as well as apps. IoMT is a well-liked technique for developing smart city solutions that eventually improve critical infrastructures, such smart healthcare. Numerous IoMT devices in smart cities employ Bluetooth technology for short-range communication because it is adaptable and resource-efficient. This research proposes novel method in urban planning in smart public healthcare system utilizing ML algorithms. The smart healthcare system is developed based on secure honeynet cloud IoT model. Here the input smart healthcare-based health monitoring data is collected and processed for missing value removal and noise removal. Then this data classified and optimized using recurrent Bi-LSTM temporal Gaussian model with whale swarm particle colony optimization. Experimental analysis is carried out in terms of detection accuracy, precision, data integrity, throughput, recall, latency. Proposed technique obtained 96% of Detection accuracy, 97% of Precision, 95% of Throughput, 88% of RECALL, 94% of LATENCY.
Keywords: Secure honeynet; Cloud IoT model; Machine learning algorithms; Health monitoring; Particle colony optimization