Acta Scientific COMPUTER SCIENCES

Research Article Volume 4 Issue 12

Adaptive Trust-based Security Model for Intrusion Detection Using Deep Learning Technique in the Cloudb>

Khalid Al Makdi1,2* and Frederick T Sheldon1

1Computer Science Department, University of Idaho, Moscow, USA
2Computer Science Department, Najran University, Najran, Saudi Arabia

*Corresponding Author: Khalid Al Makdi, Computer Science Department, University of Idaho, Moscow, USA.

Received: April 15, 2022; Published: November 29, 2022

Abstract

With the increasing numbers of Internet-connected devices, security and privacy issues are the biggest barriers to widespread cloud systems. Securing cloud systems has become a major concern for everyone, including consumers, businesses, and the government. While attacks on any system may never be completely stopped, real-time detection of threats is essential for efficient system defence. Limited research has been done on effective intrusion detection systems for IoT (Internet of Things) environments. In this paper, the authors provide a unique intrusion detection system that detects security anomalies in cloud networks using machine learning algorithms. This trust-based security paradigm acts as a service, allowing for interoperability between the many network communications protocols used in cloud systems. The authors present the system's framework and the intrusion detection procedure in detail. The proposed intrusion detection system is tested on real network traces for proof-of-concept and simulation for scalability using Deep Learning techniques and XGboost algorithm. The results shows 96% accuracy and proves that the suggested intrusion detection system is capable of effectively detecting real-world intrusions.

Keywords: Internet of Things (IoT); Cloud Computing; Software

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Citation

Citation: Khalid Al Makdi and Frederick T Sheldon. “Adaptive Trust-based Security Model for Intrusion Detection Using Deep Learning Technique in the Cloud".Acta Scientific Computer Sciences 4.12 (2022): 83-95.

Copyright

Copyright: © 2022 Khalid Al Makdi and Frederick T Sheldon. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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