DOI : https://doi.org/10.62527/ijasce.6.3.202

Distributed Denial-Of-Service Attack Detection Using One Dimention Convolutional Neural Network in Airline Reservation Systems (ARS)

Dhurgham Kareem Gharkan (1), Bahaa Kareem Mohammed (2), Hussein Ali Salah (3), Mariana Mocanu (4)
(1) Department of Medical Instruments Techniques, Middle Technical University, Technical Institute Kut, Baghdad, Iraq
(2) Department of Medical Instruments Techniques, Middle Technical University, Technical Institute Kut, Baghdad, Iraq
(3) Department of Computer Systems, Middle Technical University, Technical Institute- Suwaira, Baghdad, Iraq
(4) Computer Science Department, National University for Science and Technology Politehnica Bucharest, Romania
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How to cite (IJASEIT) :
Kareem Gharkan , D., Kareem Mohammed, B., Ali Salah, H., & Mocanu, M. (2024). Distributed Denial-Of-Service Attack Detection Using One Dimention Convolutional Neural Network in Airline Reservation Systems (ARS). International Journal of Advanced Science Computing and Engineering, 6(3), 123–128. https://doi.org/10.62527/ijasce.6.3.202
Citation Format :

A prevalent and perilous in the contemporary are Distributed Denial of Service (DDoS) attacks. in which attackers attempted to prevent authorized users from accessing internet services by deploying many attack workstations. This research presents a detection approach based on One Dimension Convolutional Neural Networks, which has created an innovative approach for detecting DDoS attacks that addresses the limitations of conventional methods. The primary objective of this study was to analyze and detect DDoS attacks through the examination of a dataset about the booking of airline tickets. The present investigation utilized the APA-DDoS dataset, comprising two discrete categories: benign traffic and DDoS traffic. Wireshark was utilized to simulate airline data as well. Utilized as one-dimension convolutional neural network (1D CNN) technology, the model achieved an accuracy rating of 99.5%. The experimental outcomes demonstrated that the proposed model effectively and consistently identified DDoS attacks. Solid ability to differentiate between legitimate and malicious traffic has been exhibited by the system, thereby ensuring network security.

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