Research Article
Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments
@INPROCEEDINGS{10.1007/978-3-031-36574-4_21, author={Syed Rizvi and Mark Scanlon and Jimmy McGibney and John Sheppard}, title={Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments}, proceedings={Digital Forensics and Cyber Crime. 13th EAI International Conference, ICDF2C 2022, Boston, MA, November 16-18, 2022, Proceedings}, proceedings_a={ICDF2C}, year={2023}, month={7}, keywords={Intrusion Detection Systems Dilated Causal Neural Network Network Investigation}, doi={10.1007/978-3-031-36574-4_21} }- Syed Rizvi
Mark Scanlon
Jimmy McGibney
John Sheppard
Year: 2023
Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments
ICDF2C
Springer
DOI: 10.1007/978-3-031-36574-4_21
Abstract
Network intrusion detection systems (IDS) examine network packets and alert system administrators and investigators to low-level security violations. In large networks, these reports become unmanageable. To create flexible and effective intrusion detection systems for unpredictable attacks, there are several challenges to overcome. Much work has been done on the use of deep learning techniques in IDS; however, substantial computational resources and processing time are often required. In this paper, a 1D-Dilated Causal Neural Network (1D-DCNN) based IDS for binary classification is employed. The dilated convolution with a dilation rate of 2 is introduced to compensate the max pooling layer, preventing the information loss imposed by pooling and down-sampling. The dilated convolution can also expand its receptive field to gather additional contextual data. To assess the efficacy of the suggested solution, experiments were conducted on two popular publicly available datasets, namely CIC-IDS2017 and CSE-CIC-IDS2018. Simulation outcomes show that the 1D-DCNN based method outperforms some existing deep learning approaches in terms of accuracy. The proposed model attained a high precision with malicious attack detection rate accuracy of 99.7% for CIC-IDS2017 and 99.98% for CSE-CIC-IDS2018.
