IDS for IoT to detect DDoS attacks using BiLSTM and cGAN

Krishnan, Hrishikesh (2024) IDS for IoT to detect DDoS attacks using BiLSTM and cGAN. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (2MB) | Preview

Abstract

The IoT devices have transformed the industries and the lives of everyone significantly and the IoT technology has been widely accepted by many homes to make their life easier. This huge rise in the use of IoT devices for everyday tasks and the architecture of these devices made them an attractable target for the attackers. The small architecture and limited computational capacity make them fall for DDoS attacks easily. This invites the need for an intrusion detection system to detect DDoS attacks. This work aims to develop a Bidirectional LSTM model integrated with Conditional GAN for data augmentation to detect DDoS attacks in IoT devices. The model is then evaluated, and it showed excellent results including accuracy 88.6%, precision 88.65%, recall 88.6% and f1 score 88.4%.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Khan, Imran UNSPECIFIED
Uncontrolled Keywords:	IDS; IoT; DDoS; BiLSTM; cGAN
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
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	30 Jul 2025 10:12
Last Modified:	30 Jul 2025 10:12
URI:	https://norma.ncirl.ie/id/eprint/8330

Actions (login required)

View Item