Mathew Nicho
Assessing IoT intrusion detection computational costs when using a convolutional neural network.
Nicho, Mathew; Cusack, Brian; McDermott, Christopher D.; Girija, Shini
Abstract
IoT systems face vulnerabilities due to their data processing requirements and resource constraints. With 13 billion connected devices globally, this research investigates the economic viability of AI-based intrusion detection systems (IDSs), specifically analyzing the automation costs of implementing a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) for classifying malicious sensor traffic. This study introduces an innovative framework that evaluates six distinct architectural components of CNN and LSTM: image input processing, convolutional layer operations, max pooling layer functionality, fully connected layer characteristics, softmax output activation, and class determination mechanisms. The framework employs six metrics: matrix size, feature vector number, input vector size, output vector size, and number of runs for dual data points. Experiments on the IoT-23 dataset showed our proposed CNN model outperformed LSTM, achieving 93% accuracy for binary classification and 96% for multi-class classification. The trained CNN demonstrated predictable resource utilization with increasing classification complexity, providing a framework for quantifying IoT IDS costs. The proposed framework provides a systematic methodology for evaluating machine learning classifiers in IoT environments, using quantitative metrics to assess implementation and operational costs, enabling data-driven selection of optimal security solutions based on specific deployment constraints.
Citation
NICHO, M., CUSACK, B., MCDERMOTT, C.D. and GIRIJA, S. 2025. Assessing IoT intrusion detection computational costs when using a convolutional neural network. Information security journal [online], Latest Articles. Available from: https://doi.org/10.1080/19393555.2025.2496327
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 24, 2025 |
Online Publication Date | Apr 24, 2025 |
Deposit Date | May 12, 2025 |
Publicly Available Date | May 12, 2025 |
Journal | Information security journal |
Print ISSN | 1939-3555 |
Electronic ISSN | 1939-3547 |
Publisher | Taylor and Francis |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.1080/19393555.2025.2496327 |
Keywords | Artificial intelligence; Convoluted neural network; Cost evaluation; Internet of things; Network security |
Public URL | https://rgu-repository.worktribe.com/output/2835894 |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
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