Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset
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 device...
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| Language: | English |
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Universitas Kristen Satya Wacana
2024-11-01
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| Series: | Techne |
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| Online Access: | https://ojs.jurnaltechne.org/index.php/techne/article/view/467 |
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| author | Sahrul Fahrezi Fahrezi Adhitya Nugraha Ardytha Luthfiarta Nauval Dwi Primadya |
| author_facet | Sahrul Fahrezi Fahrezi Adhitya Nugraha Ardytha Luthfiarta Nauval Dwi Primadya |
| author_sort | Sahrul Fahrezi Fahrezi |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-a6fbb4a3874e472f8920e17d6e2e52ab |
| institution | OA Journals |
| issn | 1412-8292 2615-7772 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Universitas Kristen Satya Wacana |
| record_format | Article |
| series | Techne |
| spelling | doaj-art-a6fbb4a3874e472f8920e17d6e2e52ab2025-08-20T02:06:58ZengUniversitas Kristen Satya WacanaTechne1412-82922615-77722024-11-0123210.31358/techne.v23i2.467Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 DatasetSahrul Fahrezi Fahrezi0Adhitya Nugraha1Ardytha Luthfiarta2Nauval Dwi Primadya3Universitas Dian NuswantoroUniversitas Dian NuswantoroUniversitas Dian NuswantoroUniversitas Dian Nuswantoro 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. https://ojs.jurnaltechne.org/index.php/techne/article/view/467AdaBoostGridSearchCVIoTUndersampling |
| spellingShingle | Sahrul Fahrezi Fahrezi Adhitya Nugraha Ardytha Luthfiarta Nauval Dwi Primadya Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset Techne AdaBoost GridSearchCV IoT Undersampling |
| title | Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset |
| title_full | Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset |
| title_fullStr | Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset |
| title_full_unstemmed | Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset |
| title_short | Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset |
| title_sort | optimizing performance of adaboost algorithm through undersampling and hyperparameter tuning on ciciot 2023 dataset |
| topic | AdaBoost GridSearchCV IoT Undersampling |
| url | https://ojs.jurnaltechne.org/index.php/techne/article/view/467 |
| work_keys_str_mv | AT sahrulfahrezifahrezi optimizingperformanceofadaboostalgorithmthroughundersamplingandhyperparametertuningonciciot2023dataset AT adhityanugraha optimizingperformanceofadaboostalgorithmthroughundersamplingandhyperparametertuningonciciot2023dataset AT ardythaluthfiarta optimizingperformanceofadaboostalgorithmthroughundersamplingandhyperparametertuningonciciot2023dataset AT nauvaldwiprimadya optimizingperformanceofadaboostalgorithmthroughundersamplingandhyperparametertuningonciciot2023dataset |