A Consistent Augmented Stacking Polynomial Optimized Tool (ASPOT) for Improving Security of Cloud-IoT Systems

Authors

  • Divya Ramachandran Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, 624622 Tamil Nadu, India
  • R. Chithambaramani Department of CSE, School of Computing, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600062 Tamil Nadu, India
  • S. Sankar Ganesh Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Ghanpur, Medchal, 501301 Malkajgiri Telangana, India
  • M. Dilli Babu Department of information Technology, Panimalar engineering college, Poonamallee, Chennai-600123, Tamil Nadu, India

DOI:

https://doi.org/10.37934/araset.37.1.1636

Keywords:

Cloud, Internet of Things (IoT), Security, Intrusion detection system (IDS), Deep learning, Augmented stacking polynomial optimized tool (ASPOT), Binary sand cat swarm optimization (BSCSO)

Abstract

The cloud computing transforms information technology by offering end users simulated, flexible resources on demand that require fewer resources and facilities giving them greater flexibility. These materials are delivered over the Internet using predefined networking protocols, regulations, and styles, and they are overseen by various management groups. There are flaws and vulnerabilities in the underlying technology and legacy protocols that could allow an attacker to get access. A recent assessment of the literature led to the conclusion that most intelligence algorithms have a number of complex issues, including a high false prediction rate, difficulty classifying threats, high processing costs, and system load. Hence, the proposed work aims to develop an innovative and lightweight Augmented Stacking Polynomial Optimized Tool (ASPOT) for strengthening the cloud-IoT system security against modern cyberattacks with a accuracy of 99%. The current study uses the lightweight Deep Augmented Preprocessing Model (DAPM) to clean and normalize the input cyber-attack dataset by executing transformation and normalization operations on it. Furthermore, the Binary Sand Cat Swarm Optimization (BSCSO) technique is utilized to identify the most significant and relevant features of the normalized dataset in an optimal manner. Moreover, the class of assaults is promptly and precisely identified from the given data by applying the Deep Stacking Polynomial Learning Network (DSPLN) technology. The effectiveness and results of the proposed ASPOT method are analysed with the use of current cyber-attack statistics and a variety of assessment metrics.

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Author Biographies

Divya Ramachandran, Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, 624622 Tamil Nadu, India

drdivyarphd@gmail.com

R. Chithambaramani, Department of CSE, School of Computing, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600062 Tamil Nadu, India

drchithambaramanir@veltech.edu.in

S. Sankar Ganesh, Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Ghanpur, Medchal, 501301 Malkajgiri Telangana, India

sankar2017vmu@gmail.com

M. Dilli Babu, Department of information Technology, Panimalar engineering college, Poonamallee, Chennai-600123, Tamil Nadu, India

deenshadilli@gmail.com         

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Published

2024-01-09

How to Cite

Divya Ramachandran, R. Chithambaramani, S. Sankar Ganesh, & M. Dilli Babu. (2024). A Consistent Augmented Stacking Polynomial Optimized Tool (ASPOT) for Improving Security of Cloud-IoT Systems. Journal of Advanced Research in Applied Sciences and Engineering Technology, 37(1), 16–36. https://doi.org/10.37934/araset.37.1.1636

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