Work

AI-driven energy-efficient cybersecurity frameworks for sustainable digital infrastructures

Year: 2025

Type: article

Abstract: Cybersecurity in digital infrastructures faces energy limitations, especially in IoT systems. Traditional models emphasize accuracy but lack efficiency, making them unsuitable for edge deployment. Existing methods struggle to balance performance and energy use, particularly with high-dimensional, imbalanced data. The objective is to develop an energy-conscious, adaptive cybersecurity framework capable of delivering robust intrusion detection while minimizing computational overhead. A novel hybrid model named Dynamic Beetle Antennae Search–Mutated Adaptive Weighted Random Forest Tree (DynBAS-AWRF Tree) is introduced, integrating a dynamic feature selection and tuning mechanism with a highly adaptable, weighted ensemble classifier. The DynBAS component performs intelligent feature subset selection and hyperparameter tuning through directional sensing and mutation-enhanced search strategies. The AWRF Tree adapts its structure and weighting in response to class distribution and feature relevance, reducing complexity while improving sensitivity to minority attacks. Evaluation utilizes labeled intrusion detection datasets containing network flow and system behavior logs representative of smart IoT and edge environments. Min-max normalization and one-hot encoding are applied to standardize and structure the input data for optimal model ingestion. Linear Discriminant Analysis (LDA) reduces dimensionality while preserving class separability. DynBAS selects optimal feature sets and hyperparameters, while AWRF Tree dynamically adjusts node weights and tree depth, maintaining efficiency and adaptability. The hybrid model achieves competitive detection accuracy with significantly reduced feature usage by 45 % and increased energy efficiency from 50 % to 85 %. The DynBAS-AWRF Tree framework enables a scalable and sustainable cybersecurity solution suitable for deployment in constrained digital infrastructures.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.107717

Authors: Saravanan T R, Bhuvan Unhelkar, Siva Shankar, Nagarajan G

Institutions: University of South Florida, Sathyabama Institute of Science and Technology

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,940