TELKOMNIKA Telecommunication Computing Electronics and Control
Deep learning approach to DDoS attack with imbalanced data at the application layer

TELKOMNIKA Telecommunication Computing Electronics and Control
Deep learning approach to DDoS attack with imbalanced data at the application layer

ADASYN
Application layer
DDoS
Deep learning
LDAP
SMOTE

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.

Rahmad Gunawan, Hadhrami Ab Ghani, Nurulaqilla Khamis, Januar Al Amien, Edi Ismanto

http://telkomnika.uad.ac.id

Mar 25, 2023

peri irawan

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