Hybrid AI intrusion detection: balancing accuracy and efficiency

Joshi, VR, Assa-Agyei, K ORCID: https://orcid.org/0000-0003-0043-3626, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X and Qasem, SN, 2025. Hybrid AI intrusion detection: balancing accuracy and efficiency. Sensors, 25 (24): 7564. ISSN 1424-8220

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Abstract

The Internet of Things (IoT) has transformed industries, healthcare, and smart environments, but introduces severe security threats due to resource constraints, weak protocols, and heterogeneous infrastructures. Traditional Intrusion Detection Systems (IDS) fail to address critical challenges including scalability across billions of devices, interoperability among diverse protocols, real-time responsiveness under strict latency, data privacy in distributed edge networks, and high false positives in imbalanced traffic. This study provides a systematic comparative evaluation of three representative AI models, CNN-BiLSTM, Random Forest, and XGBoost for IoT intrusion detection on the NSL-KDD and UNSW-NB15 datasets. The analysis quantifies the achievable detection performance and inference latency of each approach, revealing a clear accuracy–latency trade-off that can guide practical model selection: CNN-BiLSTM offers the highest detection capability (F1 up to 0.986) at the cost of higher computational overhead, whereas XGBoost and Random Forest deliver competitive accuracy with significantly lower inference latency (sub-millisecond on conventional hardware). These empirical insights support informed deployment decisions in heterogeneous IoT environments where accuracy-critical gateways and latency-critical sensors coexist.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Joshi, V.R., Assa-Agyei, K., Al-Hadhrami, T. and Qasem, S.N.
Publisher:	MDPI
Date:	12 December 2025
Volume:	25
Number:	24
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s25247564 DOI 2558684 Other
Rights:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	20 Jan 2026 14:09
Last Modified:	20 Jan 2026 14:09
URI:	https://irep.ntu.ac.uk/id/eprint/55084

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