Improving Network and IoT Intrusion Detection Through Machine Learning Algorithms

Sriramulu Deenadayala Babu, Prasanth (2024) Improving Network and IoT Intrusion Detection Through Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

Widespread and increased cyberattack against Internet of Things (IoT) are causing enormous range of problem for individual and organizations. The growing need for these services has a possibility to contain anomalies in the IoT data network has emerged as a key challenge. This research evaluates machine learning algorithms for detecting both traditional network intrusion and IoT network logs. Two datasets have been used IoT network log and Network Intrusion log dataset to classify various models on the performance metrics. To deal the issue of class imbalance and scalability nature in network and IoT dataset, Feature selection like Random Forest (RF), Correlation Coefficient and Cross-Validation & Regularization has achieved 99%, by improving this real-time processing combined with effective anomaly detection ensures threats are identified and mitigated quickly. Supervised learning models Decision Tree and KNN model has shown high accuracy of 99%. The findings show the capacity for modifying the machine learning technique to achieve high accuracy to identify labeled malicious in network traffic and IoT logs.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	28 Jul 2025 11:38
Last Modified:	28 Jul 2025 11:38
URI:	https://norma.ncirl.ie/id/eprint/8264

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