Evaluation and optimization of the naive bayes algorithm for intrusion detection systems using the USB-IDS-1 dataset
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| Titel: | Evaluation and optimization of the naive bayes algorithm for intrusion detection systems using the USB-IDS-1 dataset |
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| Autoren: | Nurbek Konyrbaev, Yevheniy Nikitenko, Vadym Shtanko, Valerii Lakhno, Zharasbek Baishemirov, Sabit Ibadulla, Asem Galymzhankyzy, Erkebula Myrzabek |
| Quelle: | Eastern-European Journal of Enterprise Technologies; Vol. 6 No. 2 (132) (2024): Information technology. Industry control systems; 74-82 Eastern-European Journal of Enterprise Technologies; Том 6 № 2 (132) (2024): Інформаційні технології. Системи управління в промисловості; 74-82 |
| Verlagsinformationen: | Private Company Technology Center, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | python, системи виявлення вторгнень, machine learning, intrusion detection systems (IDS), наївний баєсів метод, набір даних USB-IDS-1, Denial of Service (DoS) attacks, атаки на відмову в обслуговуванні, машинне навчання, USB-IDS-1 dataset, Naive Bayes method, Python |
| Beschreibung: | This study takes a look into the application of the Naive Bayes machine learning algorithm to enhance the accuracy of Intrusion Detection Systems (IDS). The primary focus is to assess the algorithm's performance in detecting various types of network attacks, particularly Denial of Service (DoS) attacks. This research proposes using Naive Bayes to improve intrusion detection systems that struggle to keep pace with evolving cyber threats. This study evaluated the efficiency scores of the Naive Bayes classifying model for two different dependency scenarios and identified strong and weak properties of this model. The Naive Bayes classifier demonstrated satisfactory results in detecting network intrusions, especially in binary classification scenarios where the goal is to distinguish normative and malicious traffic due to its simplicity and efficiency. However, its performance declined in multi-class classification tasks, where multiple types of attacks need to be differentiated. The study also highlighted the importance of data quality and quantity in training machine learning models because of the impact of those parameters on the model efficiency. The USB-IDS-1 dataset, while useful, has limitations in terms of the variety of attacks. Using datasets with a wider range of attack types could significantly improve the accuracy of IDS. The findings of this research can be applied to such domains as network security, cybersecurity, and data science. The Naive Bayes classifier can be integrated into IDS systems to enhance their ability to detect and respond to cyber threats. However, it is essential to consider the limitations of the algorithm and the specific conditions of its environment. To maximize the effectiveness of the Naive Bayes classifier, it could be promising to optimize and normalize the data to improve the accuracy of the model and combine Naive Bayes with the other machine learning algorithms to address its limitations |
| Publikationsart: | Article |
| Dateibeschreibung: | application/pdf |
| ISSN: | 1729-4061 1729-3774 |
| DOI: | 10.15587/1729-4061.2024.317471 |
| Zugangs-URL: | https://journals.uran.ua/eejet/article/view/317471 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....d546a43c275cd5fa3a86c55e752be9eb |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | This study takes a look into the application of the Naive Bayes machine learning algorithm to enhance the accuracy of Intrusion Detection Systems (IDS). The primary focus is to assess the algorithm's performance in detecting various types of network attacks, particularly Denial of Service (DoS) attacks. This research proposes using Naive Bayes to improve intrusion detection systems that struggle to keep pace with evolving cyber threats. This study evaluated the efficiency scores of the Naive Bayes classifying model for two different dependency scenarios and identified strong and weak properties of this model. The Naive Bayes classifier demonstrated satisfactory results in detecting network intrusions, especially in binary classification scenarios where the goal is to distinguish normative and malicious traffic due to its simplicity and efficiency. However, its performance declined in multi-class classification tasks, where multiple types of attacks need to be differentiated. The study also highlighted the importance of data quality and quantity in training machine learning models because of the impact of those parameters on the model efficiency. The USB-IDS-1 dataset, while useful, has limitations in terms of the variety of attacks. Using datasets with a wider range of attack types could significantly improve the accuracy of IDS. The findings of this research can be applied to such domains as network security, cybersecurity, and data science. The Naive Bayes classifier can be integrated into IDS systems to enhance their ability to detect and respond to cyber threats. However, it is essential to consider the limitations of the algorithm and the specific conditions of its environment. To maximize the effectiveness of the Naive Bayes classifier, it could be promising to optimize and normalize the data to improve the accuracy of the model and combine Naive Bayes with the other machine learning algorithms to address its limitations |
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| ISSN: | 17294061 17293774 |
| DOI: | 10.15587/1729-4061.2024.317471 |