An Adaptive Lightweight Energy Efficient AI Assisted Nomaly Detection with Real Time Threat Mitigation for Resource Constrained Wireless Networks | Journal of Engineering Strategy and Innovation | JESI
An Adaptive Lightweight Energy Efficient AI Assisted Nomaly Detection with Real Time Threat Mitigation for Resource Constrained Wireless Networks
Author(s) RegistrySivakami S, Ariharan K, Kishore Raj D K
Volume & IssueVol 1, Iss 01
Acceptance Date27 Jun 2026
Publication Date21 Jul 2026
Digital DOI Handle—
Abstract
Because of their restricted processing power, memory capacity, and battery limitations, resource-constrained wireless networks are more vulnerable to sophisticated cyberattacks. For wireless networks with limited resources, this study suggests a lightweight AI-assisted adaptive anomaly detection framework combined with an energy-efficient real-time threat mitigation mechanism. The suggested framework minimizes computing complexity by effectively analyzing both global traffic dependencies and sequential attack patterns using a lightweight hybrid Transformer-Gated Recurrent Unit (Transformer-GRU) model. In order to minimize false alarms, an adaptive anomaly threshold method dynamically modifies detection sensitivity in response to shifting network traffic patterns. In order to maintain network lifetime, an energyaware mitigation technique also optimizes communication routing and selectively isolates harmful nodes. The suggested framework delivers better detection accuracy, a lower false positive rate, minimal computing overhead, and increased energy economy, according to experimental evaluation.
Keywords
Resource constrained networkssecurityenergy efficiencyanomaly detectionGated Recurrent Unit (GRU).
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An Adaptive Lightweight Energy Efficient AI Assisted Nomaly Detection with Real Time Threat Mitigation for Resource Constrained Wireless Networks
S. Sivakami, K. Ariharan, D K. Kishore Raj, "An Adaptive Lightweight Energy Efficient AI Assisted Nomaly Detection with Real Time Threat Mitigation for Resource Constrained Wireless Networks," Journal of Engineering Strategy and Innovation (JESI), vol. 1, no. 1, pp. 40–47, 2026.
