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

Detecting DDoS in Network Traffic with Deep Learning

Rusyaidi, Muhammad, Jaf, Sardar and Zunaidi, Ibrahim (2022) Detecting DDoS in Network Traffic with Deep Learning. The International Journal of Advanced Computer Science and Applications (IJACSA), 13 (1). (In Press)

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.

Detecting Distributed Denial of Service (002).pdf - Accepted Version
Available under License Creative Commons Attribution.

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More Information

Depositing User: Sardar Jaf


Item ID: 14456

Users with ORCIDS

ORCID for Sardar Jaf: ORCID iD
ORCID for Ibrahim Zunaidi: ORCID iD

Catalogue record

Date Deposited: 27 Jan 2022 15:09
Last Modified: 27 Jan 2022 15:15


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

University Divisions

Faculty of Technology > School of Computer Science


Computing > Cybersecurity
Computing > Artificial Intelligence

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