Enhancing IoT Anomaly Detection Model for Serverless Cloud Environment

Zhang, Danni (2023) Enhancing IoT Anomaly Detection Model for Serverless Cloud Environment. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for effective anomaly detection mechanisms within the serverless cloud environment. This study delves into exploring and implementing diverse anomaly detection models, including Decision Tree Classifier, Logistic Regression, Random Forest Classifier, Convolutional Neural Network with Long Short-Term Memory (CNN with LSTM) and Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN with BiLSTM) to identify the most effective model for this purpose. Through comprehensive experimentation and evaluation, it was found that CNN with BiLSTM outperforms other models, demonstrating an impressive accuracy of approximately 83%. The bidirectional aspect of the BiLSTM layer permits to the model to capture both past and future context, enabling a better understanding of the sequential nature of IoT device behaviour. The implications of this research are substantial, underscoring the significance of leveraging advanced deep learning architectures, particularly CNN with BiLSTM, for anomaly detection in IoT applications. The superior performance of this model suggests its potential to significantly enhance IoT security, cloud computing and reliability. The findings of this study pave the way for future research and practical implementations, propelling the domain of IoT anomaly detection forward and fostering a safer and more resilient IoT ecosystem.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kazmi, Aqeel UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	11 Apr 2025 13:28
Last Modified:	11 Apr 2025 13:28
URI:	https://norma.ncirl.ie/id/eprint/7424

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