Document Type
Article
Source of Publication
International Journal of Computers and Applications
Publication Date
9-10-2024
Abstract
The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but it costs more to implement and operate than a traditional classifier. This research proposes an economic classifier model for deploying suitable AI-based intrusion detection classifiers in IoT environments. The model was empirically validated on the IoT-23 dataset using KNN and CNN. This study closes a gap in prior research that mostly concentrated on technical elements by incorporating economic factors into the evaluation of AI algorithms for IoT intrusion detection. This research thus evaluated the economic implications of deploying AI-based intrusion detection systems in IoT environments, considering performance metrics, implementation costs, and the cost of classifier discrimination choices. Researchers and practitioners should focus on the cost–benefit trade-offs of any artificial intelligence application for intrusion detection, recommending an economic evaluation and task fit assessment before adopting automated solutions or classifiers for IoT intrusion detection, particularly in large-scale industrial settings that involve active attacks.
DOI Link
ISSN
Publisher
Informa UK Limited
Disciplines
Computer Sciences
Keywords
classifier costs, cost evaluation, cyber security, Internet of things, pattern recognition, threat detection
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Nicho, Mathew; Cusack, Brian; Girija, Shini; and Arachchilage, Nalin, "Evaluating the cost of classifier discrimination choices for IoT sensor attack detection" (2024). All Works. 6793.
https://zuscholars.zu.ac.ae/works/6793
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series