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Assessing IoT intrusion detection computational costs when using a convolutional neural network.

Nicho, Mathew; Cusack, Brian; McDermott, Christopher D.; Girija, Shini

Authors

Mathew Nicho

Brian Cusack

Shini Girija



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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