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All Journal International Journal of Advances in Applied Sciences
Ijaz Ahmad
Majan College in Muscat
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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An observational mechanism for detection of distributed denial-of-service attacks Norliza Katuk; Mohamad Sabri Sinal; Mohammed Gamal Ahmed Al-Samman; Ijaz Ahmad
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp121-132

Abstract

This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results suggested that the detection model built based on convolutional deep-learning neural networks and relevant network traffic features provided 97.2% detection accuracy. The study designed a holistic mechanism that considers the systematic network traffic data management for continuous monitoring and good performance of DDoS attack detection. The proposed mechanism could provide a solution for network traffic data management and enhance the existing methods for DDoS attack detection. In addition, it generally contributes to the cybersecurity body of knowledge.
Co-Authors Mohamad Sabri Sinal Mohammed Gamal Ahmed Al-Samman Norliza Katuk