STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Wireless Security and Networks Review Article

Unified Ensemble Techniques for Enhanced DDoS Attack Prevention and Detection

  1. Purushotam Naidu K. Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), GVP College of Engineering for Women, Visakhapatnam
  2. Telukala Anitha Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), GVP College of Engineering for Women, Visakhapatnam
  3. Inaganti Poshitha Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), GVP College of Engineering for Women, Visakhapatnam
  4. Yetendriya Lamani K. Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), GVP College of Engineering for Women, Visakhapatnam
  5. G. Satvika Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), GVP College of Engineering for Women, Visakhapatnam
  6. G. Sireesha Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), GVP College of Engineering for Women, Visakhapatnam

Abstract

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 organizations worldwide, of data breaches where the terms denial-of-service (DoS) and DDoS are consistently present in the stolen material. Network security is seriously threatened by DoS attacks. They have the ability to overburden a server or network, rendering it inaccessible to authorized users. DoS assaults are identified and stopped by means of network intrusion detection systems, or Network Detection Systems (NDS). However, because DoS assaults frequently employ dispersed and coordinated techniques, conventional network intrusion detection system (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 more accurate forecast, ensemble approaches integrate the predictions of numerous classifiers. They may thereby overcome the shortcomings of individual classifiers, making them ideal for identifying DoS 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, to detect DoS attacks.

Keywords

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