Hybrid Model-Based Cauchy and Machine Learning Algorithms for IoT-Intrusion Detection System.

The internet of things (IoT) cybersecurity presents a crucial challenge in our daily lives. An intrusion detection system (IDS) is valuable for protecting IoT data against malicious attacks. Moreover, intrusion datasets are often imbalanced in the number of attacks, increasing the bias in machine learning towards classes with high frequency. Normally, this case affects a training model's performance and ability to make a correct prediction. This paper presents a hybrid model that merges dynamic evolving cauchy clustering (DECS) with ranking classification. The DECS model operates based on the self-similarity principle, strategically distributing data into clusters to counteract the impact of imbalanced data. Furthermore, the rank classification algorithm predicts classes for new attacks. The NF-ToN-IoT dataset was used to test the validity performance of the proposed model and compared with standard machine learning algorithms (K-nearest neighbours, random forest, and decision tree). The proposed model outperforms standard cauchy clustering regarding mean square error (MSE), exhibiting a noteworthy reduction to 0.0534. Furthermore, the silhouette score, which indicates clustering quality, notably improved, reaching 0.4707. Additionally, the proposed model attained an accuracy rate of 67.77% and an F1-score of 76.52%, while the standard Random Forest achieved better accuracy at 66.34% and an F1-score of 68.48%. [ABSTRACT FROM AUTHOR]