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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA Digital Zone: Jurnal Teknologi Informasi dan Komunikasi INTEK: Informatika dan Teknologi Informasi Journal of Information Systems and Informatics Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech)
Januar Al Amien
Universitas Muhammadiyah Riau
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Deep learning approach to DDoS attack with imbalanced data at the application layer Rahmad Gunawan; Hadhrami Ab Ghani; Nurulaqilla Khamis; Januar Al Amien; Edi Ismanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24857

Abstract

A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
Co-Authors Aditya Quantano Surbakti Aminuyati Baidarus Cuncun Wibowo Denin Nur Ichsan Desti Mualfah Edi Ismanto Evans Fuad Febby Apri Wenando Gunawan, Rahmad Hadhrami Ab Ghani Hadhrami Ab Ghani Harun Mukhtar Hasanatul Fu'adah Amran Hayami, Regiolina Ilham Firdaus Indra Saputra M Arif Rucyat Maruba Hutabalian Muhammad Ilham Akbar Muhammad Waqi Azizi Nurul Izrin Md Saleh Nurul Izrin Md Saleh Nurul Izrin Md Saleh Nurulaqilla Khamis Rahmad Firdaus Riski Hakiki Soni Suryanto Suryanto Ulva Elviani Unik, Mitra Wulanda Pratiwi Yulia Fatma