Intelligent Green Efficiency for Intrusion Detection

TL;DR

This paper evaluates how programming languages and feature selection methods impact the computational efficiency and environmental sustainability of AI models used in network intrusion detection.

Contribution

It introduces an assessment of programming languages and feature selection techniques to enhance green AI performance in intrusion detection tasks.

Findings

Feature selection improves computational efficiency without losing accuracy.

Python and R are advantageous due to rich AI libraries.

Efficient AI systems can be designed for sustainability and reliability.

Abstract

Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in terms of the energy consumption and carbon footprint of Machine Learning (ML) and Deep Learning (DL) models, making essential investigate Green AI, an attempt to reduce the climate impact of AI systems. This paper presents an assessment of different programming languages and Feature Selection (FS) methods to improve computation performance of AI focusing on Network Intrusion Detection (NID) and cyber-attack classification tasks. Experiments were conducted using five ML models - Random Forest, XGBoost, LightGBM, Multi-Layer Perceptron, and Long Short-Term Memory - implemented in four programming languages - Python, Java, R, and Rust - along with three FS methods - Information Gain, Recursive Feature Elimination, and Chi-Square. The obtained results demonstrated that FS plays an important role enhancing the computational efficiency of AI models without compromising detection accuracy, highlighting languages like Python and R, that benefit from a rich AI libraries environment. These conclusions can be useful to design efficient and sustainable AI systems that still provide a good generalization and a reliable detection.