Cyber Security Intrusion Detection Deep Learning Model for Internet of Things (IoT)

Bhattacharjee, Abhirup (2022) Cyber Security Intrusion Detection Deep Learning Model for Internet of Things (IoT). Masters thesis, Dublin, National College of Ireland.

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Abstract

The current paper focuses on a detailed investigation of deploying deep learning techniques for IoT system intrusion detection utilizing specific neural network models. The current research represents a future to be conducted over NSL-KDD dataset archived by UNSW lab. This paper's primary objective is to develop a Convolutional Neural Network-based Intrusion Detection System (IDS) to improve internet security. The recommended IDS architecture classifies all network packet traffic into types that are benign or malicious in order to identify network intrusions. For the suggested approach, CNN, DNN, Logistic Regression, Adaboost, and Random Forest four important experimental DL models, are taken into consideration. Performing a comparison analysis using variables like accuracy, precision, recall, F1, and run time is a key component of the study.

Item Type:	Thesis (Masters)
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
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Clara Chan
Date Deposited:	24 Nov 2022 19:11
Last Modified:	24 Nov 2022 19:11
URI:	https://norma.ncirl.ie/id/eprint/5934

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