Inproceedings
An Evaluation of AI-Based Network Intrusion Detection in Resource-Constrained Environments
Contribution Summary
This study presents an evaluation of AI-based network intrusion detection in resource-constrained environments, focusing on the development of a novel approach that trains and deploys AI models on resource-constrained devices. The proposed approach is designed to secure networks and categorize significant traffic for later investigation, identifying and recording potential malicious attacks in real-time with minimal overhead. The evaluation employed the IoT-23 dataset and demonstrated high classification accuracy, with each of the included algorithms achieving a classification accuracy of over 99% on a representative resource-constrained device. The study contributes to the field of network intrusion detection in resource-constrained environments, providing a comprehensive evaluation of AI-based approaches and their potential applications in securing IoT and edge computing devices.
Keywords: Network Intrusion Detection Systems; Resource Constrained Environments; Internet of Things; Artificial Intelligence; Cybersecurity; IoT Forensic Readiness; Edge Computing; Deep Learning
Abstract
Internet of Things (IoT) and edge computing devices have become integral to corporate and industrial systems. These devices are prime targets for attackers due to their constant availability and potential access to sensitive data. Handling substantial data quantities, these devices pose challenges in identifying relevant forensic evidence and investigating abnormal activities. Thus, accurate network intrusion detection is crucial in these resource-constrained environments. In addition, robust IoT forensic readiness strategies are vital for effective investigation. Unlike traditional computer forensic readiness, these strategies adapt to heterogeneous architectures. This paper evaluates an approach that directly trains and deploys AI models on resource-constrained devices, securing networks and categorizing significant traffic for later investigation. The approach identifies and records potential malicious attacks in real-time with minimal overhead, suitable for constrained environments. The experimentation employed the IoT-23 dataset. The outcome of the evaluation revealed that each of the included algorithms achieved a classification accuracy of over 99% on a representative resource-constrained device.
BibTeX
@inproceedings{rizvi2023AI-IDS-Resource-Constrained,
author={Rizvi, Syed and Scanlon, Mark and McGibney, Jimmy and Sheppard, John},
title="{An Evaluation of AI-Based Network Intrusion Detection in Resource-Constrained Environments}",
booktitle="{14th Annual IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON)}",
address= "New York, USA",
publisher="IEEE",
year=2023,
month=10,
abstract={Internet of Things (IoT) and edge computing devices have become integral to corporate and industrial systems. These devices are prime targets for attackers due to their constant availability and potential access to sensitive data. Handling substantial data quantities, these devices pose challenges in identifying relevant forensic evidence and investigating abnormal activities. Thus, accurate network intrusion detection is crucial in these resource-constrained environments. In addition, robust IoT forensic readiness strategies are vital for effective investigation. Unlike traditional computer forensic readiness, these strategies adapt to heterogeneous architectures. This paper evaluates an approach that directly trains and deploys AI models on resource-constrained devices, securing networks and categorizing significant traffic for later investigation. The approach identifies and records potential malicious attacks in real-time with minimal overhead, suitable for constrained environments. The experimentation employed the IoT-23 dataset. The outcome of the evaluation revealed that each of the included algorithms achieved a classification accuracy of over 99% on a representative resource-constrained device.},
doi={10.1109/UEMCON59035.2023.10315971},
}