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Architecting Behavioural Anomaly Detection for Cloud-Scale Security Operations: An Interpretable Statistical Framework for SOC-Driven Cyber Threat Analytics

Justice Opara-Martins
Reseach Article

Neuromorphic Computing for Energy-Efficient Edge AI Systems

by Godfrey Perfectson Oise, Chioma Julia Onwuzo, Immunhierokene Clinton Obrorindo, Augustine Emakpor Otorokpo, Ifeoma Vivian Ngonadi
journal cover thumbnail

Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 7
Year of Publication: 2026
Authors: Godfrey Perfectson Oise, Chioma Julia Onwuzo, Immunhierokene Clinton Obrorindo, Augustine Emakpor Otorokpo, Ifeoma Vivian Ngonadi

Godfrey Perfectson Oise, Chioma Julia Onwuzo, Immunhierokene Clinton Obrorindo, Augustine Emakpor Otorokpo, Ifeoma Vivian Ngonadi . Neuromorphic Computing for Energy-Efficient Edge AI Systems. Journal of Advanced Artificial Intelligence. 2, 7 ( Oct 2026), 15-25. DOI=None

@article{ None,
author = { Godfrey Perfectson Oise, Chioma Julia Onwuzo, Immunhierokene Clinton Obrorindo, Augustine Emakpor Otorokpo, Ifeoma Vivian Ngonadi },
title = { Neuromorphic Computing for Energy-Efficient Edge AI Systems },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Oct 2026 },
volume = { 2 },
number = { 7 },
month = { Oct },
year = { 2026 },
pages = { 15-25 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume2/number7/neuromorphic-computing-for-energy-efficient-edge-ai-systems/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-10-30T22:21:59.380143+05:30
%A Godfrey Perfectson Oise
%A Chioma Julia Onwuzo
%A Immunhierokene Clinton Obrorindo
%A Augustine Emakpor Otorokpo
%A Ifeoma Vivian Ngonadi
%T Neuromorphic Computing for Energy-Efficient Edge AI Systems
%J Journal of Advanced Artificial Intelligence
%V 2
%N 7
%P 15-25
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The rapid expansion of Internet of Things (IoT) ecosystems has intensified the need for intelligent, real-time, and energy-efficient security mechanisms at the network edge. Traditional deep learning models, although highly accurate, are computationally intensive and unsuitable for deployment in resource-constrained edge environments. This study proposes a neuromorphic computing framework based on Spiking Neural Networks (SNNs) for efficient anomaly and intrusion detection in IoT systems. The model leverages spike-based temporal encoding to transform continuous network traffic data into discrete event-driven representations, enabling sparse and energy-efficient computation. A multi-layer SNN architecture utilizing leaky integrate-and-fire neurons is developed and trained using a hybrid learning strategy that combines Spike-Timing-Dependent Plasticity and surrogate gradient optimization. The framework is evaluated using the N-BaIoT dataset, which includes benign traffic and botnet attack classes such as Mirai and Gafgyt. Experimental results demonstrate that the proposed model achieves near-perfect classification performance, with accuracy exceeding 99%, while maintaining low computational overhead and reduced energy consumption. The confusion matrix and ROC analysis further confirm strong discriminative capability across all classes. Despite these promising results, the study acknowledges limitations related to dataset specificity and real-world generalizability. Overall, the findings highlight the potential of neuromorphic computing as a viable solution for scalable, low-power, and high-performance edge intelligence in IoT security applications, paving the way for future deployment in real-world environments.

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Index Terms

Computer Science
Information Sciences

Keywords

Neuromorphic Computing Spiking Neural Networks Edge AI IoT Security Anomaly Detection Energy-Efficient Computing

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