Inproceedings
Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments
Contribution Summary
This paper presents a deep learning-based network intrusion detection system (IDS) for resource-constrained environments. The proposed 1D-Dilated Causal Neural Network (1D-DCNN) model is employed for binary classification on two popular datasets, CIC-IDS2017 and CSE-CIC-IDS2018. The model's architecture is designed to compensate for the max pooling layer, preventing information loss and expanding its receptive field to gather additional contextual data. The results show that the proposed model outperforms existing deep learning approaches in terms of accuracy, achieving a high precision with malicious attack detection rate accuracy of 99.7% for CIC-IDS2017 and 99.98% for CSE-CIC-IDS2018. The model's efficiency and effectiveness make it suitable for resource-constrained environments.
Keywords: Intrusion Detection Systems; Dilated Causal Neural Network; Network Investigation; Deep Learning; Resource-Constrained Environments; Network Security; Cybersecurity; Anomaly Detection
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 a 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 downsampling. 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.
BibTeX
@inproceedings{rizvi2022DLNIDS,
author={Rizvi, Syed and Scanlon, Mark and McGibney, Jimmy and Sheppard, John},
title="{Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments}",
booktitle="{The 13th EAI International Conference on Digital Forensics and Cyber Crime}",
series = {ICDF2C '22},
year=2022,
month=11,
location={Boston, USA},
publisher={Springer},
address = {New York, NY, USA},
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 a 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 downsampling. 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.},
doi={10.1007/978-3-031-36574-4_21},
}