Abstract: 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.

Muhammad Rusyaidi, Sardar Jaf and Zunaidi Ibrahim, “Detecting Distributed Denial of Service in Network Traffic with Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130105

@article{Rusyaidi2022,
title = {Detecting Distributed Denial of Service in Network Traffic with Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130105},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130105},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Muhammad Rusyaidi and Sardar Jaf and Zunaidi Ibrahim}
}