Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{169959,
        author = {Mula Madhuri and Muthineni Mahathi and Kommineni Shamvitha and Lolakapuri Lakshmi Shreya and Kalawarala Jayasree and Thanish Kumar},
        title = {IOT Malware Detection Technique Based on Deep Learning and Natural Language Processing.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2834-2837},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169959},
        abstract = {The rapid growth of the Internet of Things (IoT) has led to increased security challenges, as traditional security measures struggle to keep pace with the complexity and diversity of IoT devices. This study addresses these concerns by exploring advanced malware classification techniques tailored for IoT environments, specifically through two models: Random Forest and Logistic Regression. Trained on 41,323 legitimate samples and 96,724 malware samples, the models were evaluated using metrics such as accuracy, F1-score, recall, and precision. Results show that the Random Forest classifier achieved a notable accuracy of 96.7%, outperforming the Logistic Regression model at 92.3%. These findings highlight the efficacy of machine learning techniques in enhancing IoT security, providing robust defenses against emerging threats. This research contributes valuable insights and practical solutions for improving malware detection within the increasingly interconnected IoT landscape.},
        keywords = {},
        month = {November},
        }