Malware Detection in IOT Systems Using Machine Learning Techniques

TL;DR

This paper presents a CNN-LSTM hybrid model for IoT malware detection, achieving high accuracy and outperforming existing methods, thereby advancing IoT security measures.

Contribution

Introduces a novel CNN-LSTM hybrid model for IoT malware detection with superior performance over traditional techniques.

Findings

Achieved 95.5% accuracy in malware detection

CNN-LSTM model outperforms existing methods

Highlights potential for enhanced IoT security

Abstract

Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.