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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Detecting Distributed Denial of Service in Network Traffic with Deep Learning

Rusyaidi, Muhammad, Jaf, Sardar and Ibrahim, Zunaidi (2022) Detecting Distributed Denial of Service in Network Traffic with Deep Learning. International Journal of Advanced Computer Science and Applications, 13 (1). pp. 34-41. ISSN 2156-5570

Item Type: Article


COVID-19 has altered the way businesses throughout the world perceive cyber security. It resulted in a series of unique cyber-crime-related conditions that impacted society and business. Distributed Denial of Service (DDoS) has dramatically increased in recent year. Automated detection of this type of attack is essential to protect business assets. In this research, we demonstrate the use of different deep learning algorithms to accurately detect DDoS attacks. We show the effectiveness of Long Short-Term Memory (LSTM) algorithms to detect DDoS attacks in computer networks with high accuracy. The LSTM algorithms have been trained and tested on the widely used NSL-KDD dataset. We empirically demonstrate our proposed model achieving high accuracy (~97.37%). We also
show the effectiveness of our model in detecting 22 different types of attacks.

IJACSA-Detecting_Distributed_Denial_of_Service.pdf - Published Version
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More Information

Additional Information: Cybersecurity; Cyber-attack; DDoS attack; machine learning; deep learning; recurrent neural networks; long short-term memory
Related URLs:
Depositing User: Sardar Jaf


Item ID: 14548
Identification Number:
ISSN: 2156-5570
Official URL:

Users with ORCIDS

ORCID for Sardar Jaf: ORCID iD

Catalogue record

Date Deposited: 14 Feb 2022 13:16
Last Modified: 14 Feb 2022 13:30


Author: Sardar Jaf ORCID iD
Author: Muhammad Rusyaidi
Author: Zunaidi Ibrahim

University Divisions

Faculty of Technology > School of Computer Science


Computing > Cybersecurity
Computing > Artificial Intelligence

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