IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning
The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitation...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5871 |
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| author | Susanto Deris Stiawan Budi Santoso Alex Onesimus Sidabutar M. Agus Syamsul Arifin Mohd Yazid Idris Rahmat Budiarto |
| author_facet | Susanto Deris Stiawan Budi Santoso Alex Onesimus Sidabutar M. Agus Syamsul Arifin Mohd Yazid Idris Rahmat Budiarto |
| author_sort | Susanto |
| collection | DOAJ |
| description | The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices. |
| format | Article |
| id | doaj-art-886347f2df494cbb897cf0bc18b5dda8 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-886347f2df494cbb897cf0bc18b5dda82025-01-13T03:30:32ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018678879810.29207/resti.v8i6.58715871IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine LearningSusanto0Deris Stiawan1Budi Santoso2Alex Onesimus Sidabutar3M. Agus Syamsul Arifin4Mohd Yazid Idris5Rahmat Budiarto6Universitas Bina InsanUniversitas SriwijayaUniversitas Bina InsanUniversitas Bina InsanUniversitas Bina InsanUniversiti Teknologi MalaysiaAlbaha UnivesityThe rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5871botnetiotfeature engineeringsofmmachine learning |
| spellingShingle | Susanto Deris Stiawan Budi Santoso Alex Onesimus Sidabutar M. Agus Syamsul Arifin Mohd Yazid Idris Rahmat Budiarto IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) botnet iot feature engineering sofm machine learning |
| title | IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning |
| title_full | IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning |
| title_fullStr | IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning |
| title_full_unstemmed | IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning |
| title_short | IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning |
| title_sort | iot security botnet detection using self organizing feature map and machine learning |
| topic | botnet iot feature engineering sofm machine learning |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5871 |
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