Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks....

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Vydáno v:International journal of information security Ročník 23; číslo 4; s. 2559 - 2581
Hlavní autoři: Otokwala, Uneneibotejit, Petrovski, Andrei, Kalutarage, Harsha
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Springer Nature B.V
Témata:
Classification
Coding and Information Theory
Communications Engineering
Compression
Computation
Computer Communication Networks
Computer Science
Critical infrastructure
Cryptology
Cybersecurity
Data compression
Embedded systems
Infrastructure
Internet
Internet of Things
Intrusion
Intrusion detection systems
Lightweight
Machine learning
Management of Computing and Information Systems
Memory
Memory devices
Memory tasks
Networks
Operating Systems
Privacy
Recall
Regular Contribution
Semi-supervised learning
Weight reduction
Internet of things
Computational cost
Feature selection
Intrusion detection
ISSN:1615-5262, 1615-5270
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Popis
Shrnutí:Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning, and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CIC-IDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall, and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12 s using only 2KB of memory.
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ISSN:1615-5262
1615-5270
DOI:10.1007/s10207-024-00855-7