Unified Ensemble Techniques for Enhanced DDoS Attack Prevention and Detection

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Year : 2024 | Volume :02 | Issue : 02 | Page : –
By
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Purushotam Naidu K.,

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Telukala Anitha,

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Inaganti Poshitha,

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Yetendriya Lamani K.,

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G. Satvika,

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G. Sireesha,

  1. Assistant Professor, Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  2. Student, Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  3. Student, Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  4. Student, Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  5. Student, Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  6. Student, Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India

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Today’s world is entirely reliant on the Internet. The internet is a worldwide information source that all users rely on, hence its accessibility is critical. There have been reports in recent years, particularly in the information and technology division of significant organisations worldwide, of data breaches where the terms DoS and DDoS are consistently present in the stolen material. Network security is seriously threatened by denial-of-service (DoS) attacks. They have the ability to overburden a server or network, rendering it inaccessible to authorised users. DoS assaults are identified and stopped by means of network intrusion detection systems, or NDS. However, because DoS assaults frequently employ dispersed and coordinated techniques, conventional NIDS have trouble identifying them. NIDS’s ability to identify DoS attacks can be enhanced by the use of ensemble methods, a machine learning methodology. In order to provide a forecast that is more accurate, ensemble approaches integrate the predictions of numerous classifiers. They may thereby overcome the shortcomings of individual classifiers, making them ideal for identifying denial-of-service assaults. The UNSW-NB15 dataset, developed by the Australian Cyber Security Centre in 2015, is used in this model’s evaluation of the effectiveness of employing well-known ensemble techniques, including Bagging, AdaBoost, Stacking, Decorate, Random Forest, and Voting, for the purpose of detecting DoS attacks.

Keywords: Machine Learning, Ensemble Classifier, Stacking, AdaBoost, Bagging, DdoS, UNSW-NB15 dataset.

[This article belongs to International Journal of Wireless Security and Networks (ijwsn)]

How to cite this article:
Purushotam Naidu K., Telukala Anitha, Inaganti Poshitha, Yetendriya Lamani K., G. Satvika, G. Sireesha. Unified Ensemble Techniques for Enhanced DDoS Attack Prevention and Detection. International Journal of Wireless Security and Networks. 2024; 02(02):-.
How to cite this URL:
Purushotam Naidu K., Telukala Anitha, Inaganti Poshitha, Yetendriya Lamani K., G. Satvika, G. Sireesha. Unified Ensemble Techniques for Enhanced DDoS Attack Prevention and Detection. International Journal of Wireless Security and Networks. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijwsn/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 05/10/2024
Accepted 22/10/2024
Published 08/11/2024
175

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