DDoS Defence in IoMT: A Hybrid CNNLSTM approach for SNORT based Intrusion Detection

Kambakaran Mathew, Misha Rose (2024) DDoS Defence in IoMT: A Hybrid CNNLSTM approach for SNORT based Intrusion Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

The advancement of IoT has expanded its application in various sectors including health sector, which leads to rise of IoMT. IoMT also transfer large amount of data between devices, therefore it is highly susceptible to cyber-attacks. This research mainly focuses on mitigation of Distributed Denial of Service (DDoS) attacks on IoMT devices by developing a hybrid Convolution (CNN) and Long Short-Term Memory (LSTM) model integrating with SNORT Network Intrusion Detection System (NIDS). The CNN-LSTM model utilize CNN for feature extraction and LSTM for pattern recognition, which provide best framework for detecting DDoS attacks in IoMT devices. The model was trained and evaluated using IoT-based ICU scenario, including both normal and malicious traffic data offering a broad view of network behaviour in healthcare. After deep learning hybrid CNN-LSTM model achieved 95% accuracy in identifying DDoS attacks with 95% of precision, 94.95% of Recall,94.99% of F1 score and 95% of Malicious score. A SNORT rule was formulated by integrating the deep learning predictions and it was tested by customized IoMT traffic.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Aleburu, Joel UNSPECIFIED
Uncontrolled Keywords:	DDoS; IoMT; CNN; LSTM; NIDS
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 10:04
Last Modified:	30 Jul 2025 10:04
URI:	https://norma.ncirl.ie/id/eprint/8328

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