Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset
DOI:
https://doi.org/10.31358/techne.v23i2.467Keywords:
AdaBoost, GridSearchCV, IoT, UndersamplingAbstract
The increasing prevalence of the Internet of Things (IoT) in various sectors presents new challenges related to security and protection against cyberattacks. The connection of IoT devices to the Internet network makes them vulnerable to various types of attacks. One approach to attacking IoT devices is to perform analysis based on network traffic using machine learning algorithms such as AdaBoost. An IoT device attack prediction model was created for the purpose of predicting IoT device attacks based on network traffic. Based on research and discussion regarding optimization of the n_estimator value and algorithm in the AdaBoost algorithm on the CICIoT 2023 dataset that has been undersampled and using the grid search cv method, the most optimal n_estimator value is 500 and the most optimal algorithm value is SAMME with an accuracy rate of 0.78 and a recall value of 0.78. This optimization underscores the significance of finetuning parameters in machine learning algorithms to enhance the effectiveness of cybersecurity measures for IoT devices.
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