Deep learning hybridization for improved malware detection in smart Internet of Things

Abstract

The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection

“…The proposed approach achieved an accuracy rate of 99.99%, a reduced error rate of 0.001% for binary classification, an accuracy rate of 99.98%, and a reduced error rate of 0.016% for multi-class classification. The study [28] presented a BEFNNet (BERT-based Feed-Forward Neural Network) framework suitable for malware detection. This study used an innovative architecture with several modules to analyze eight datasets, each representing a different kind of malware.…”