IoT Intrusion Detection with Deep Learning Techniques

It must be argued that the rising number of IoT devices has latest features they got cybersecurity threats and further robust techniques to detect and prevent these threats are needed. This paper aims to propose a novel solution to IoT cybersecurity by using the BoTNeTIoT-L01 dataset and advanced artificial neural networks. The work enhances the classification and categorization of IoT cyber threats using models such as Decision Trees, Logistic regression, LightGBM, and Deep learning. Data pre-processing is been done comprehensively by feature selection and by encoding. A specific enhancement from previous models is the further enhancement of the model performance through the inclusion of T-scores, Leaky ReLU, and Ordinal Encoder. LightGBM and Decision Trees showed superiority in the basic fields of measure such as F1 score, precision, and recall, and the study achieved high accuracy and a high recall rate in threat detection. Specifically, the proposed method achieved an accuracy of 98.76 percent and a loss of 0.034 percent, demonstrating its effectiveness. Comparing LightGBM and Decision Trees with deep learning models, it was found that while both sets of models offered the right balance of testing accuracy with computational amenities, the deep learning models were superior in terms of complexity and pattern discovery. The present study proves that using machine learning algorithms can significantly enhance IoT security; however, the information suggests that updates and changes need to be made constantly and frequently to address the emerging risks.