Enhanced the intrusion detection accuracy rate and performance using deep CNN-LSTM

Tools

Kaushik, Sweety (2021) Enhanced the intrusion detection accuracy rate and performance using deep CNN-LSTM. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (771kB) \| Preview
Preview	PDF (Configuration manual) Download (697kB) \| Preview

Abstract

The Internet of Things (IoT) is a fast-increasing worldwide network that facilitates the connection and sharing of data amongst all intelligent devices. The Internet of Things units is handled continuously. Attackers may be able to take advantage of these gadgets very fast in the next generation, making the Internet of Things a major concern. With an intrusion detection system, the Internet of Things detects IoT threats and network problems. When an intrusion into devices is involved, it is quite easy for IoT devices to be lost or damaged. The Internet of Things devices, which serve as a defence for the entire network, is sometimes neglected as being critical to the security of the network and the protection of the data collected. The purpose of this study is to propose a novel model for improving the precision and efficiency of intruder detection systems for the Internet of Things devices. The model is based on the 2017 CICIDS data set and a deep CNN-LSTM neural network.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Cybersecurity; Intrusion Detection; CNN-LSTM; 2017 CICIDS Dataset
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:	29 Oct 2021 11:40
Last Modified:	29 Oct 2021 11:40
URI:	https://norma.ncirl.ie/id/eprint/5117

Actions (login required)

View Item