Hybrid AI Framework Achieves 97.3% Accuracy in Malware Detection

Global: Hybrid AI Framework Achieves 97.3% Accuracy in Malware Detection

A new hybrid context‑aware malware detection framework (HCAMDF) has achieved an overall accuracy of 97.3% while maintaining a false‑positive rate of 1.5%, according to a study posted on arXiv.

Motivation and Background

The increasing frequency and complexity of malware attacks have reduced the effectiveness of traditional signature‑based detection methods, prompting a need for more adaptive security solutions.

Framework Architecture

HCAMDF integrates static file analysis, dynamic behavioural monitoring, and contextual metadata. Its multi‑layer design employs lightweight static classifiers, including a Long Short‑Term Memory (LSTM) model for real‑time behavioural analysis, and an ensemble risk‑scoring mechanism that aggregates predictions across layers.

Evaluation Methodology

The researchers evaluated the framework using two benchmark datasets, EMBER and CIC‑MalMem2022. Comparative experiments measured accuracy, false‑positive rate, and detection latency against several established machine‑learning and deep‑learning approaches.

Results

Experimental results indicated that HCAMDF outperformed competing methods, delivering 97.3% detection accuracy, a 1.5% false‑positive rate, and reduced detection delay.

Implications

These findings suggest that hybrid AI systems can effectively identify both known and novel malware variants, supporting real‑time protection in rapidly evolving threat environments.

Future Directions

The authors propose extending the framework to incorporate additional contextual signals and to assess performance in live network deployments.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.