Intrusion Detection System for IoT Based on Modified Random Forest Algorithm

Authors

Omar Z. Akif, Department of Computer Science, College of Education for Pure Science (Ibn al-Haitham), University of Baghdad, IraqFollow
Sura Mazin Ali, Political Science College, Mustansiriyah University, IraqFollow
Ann F. Sabih, Al-Shaab University, College of Engineering and Information Technology, Department of Artificial, Intelligence Engineering, IraqFollow
Ahmed T. Sadiq, University of Technology, Department of Computer Science, IraqFollow
S. K. Subramaniam, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer, Universiti Teknikal Malaysia Melaka, MalaysiaFollow

Abstract

An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets mentioned above and it emerges that the accuracy of the proposed system is 96.2%, which is better than the other methods in the IoTID20 Dataset, while the accuracy with the second dataset UNSW-NB15 yielded 98.85%. Lastly, using the third dataset, IoT-23, the suggested technique achieved 99.93%.

Recommended Citation

Akif, Omar Z.; Ali, Sura Mazin; Sabih, Ann F.; Sadiq, Ahmed T.; and Subramaniam, S. K. (2025) "Intrusion Detection System for IoT Based on Modified Random Forest Algorithm," Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 17.
DOI: https://doi.org/10.52866/2788-7421.1258
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/17