TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep learning with focal loss approach for attacks classification

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep learning with focal loss approach for attacks classification

Attack classification
Focal loss
Imbalanced data
Intrusion detection system
Multi-class

The rapid development of deep learning improves the detection and
classification of attacks on intrusion detection systems. However, the
unbalanced data issue increases the complexity of the architecture model. This
study proposes a novel deep learning model to overcome the problem of
classifying multi-class attacks. The deep learning model consists of two stages.
The pre-tuning stage uses automatic feature extraction with a deep
autoencoder. The second stage is fine-tuning using deep neural network
classifiers with fully connected layers. To reduce imbalanced class data, the
feature extraction was implemented using the deep autoencoder and improved
focal loss function in the classifier. The model was evaluated using 3 loss
functions, including cross-entropy, weighted cross-entropy, and focal losses.
The results could correct the class imbalance in deep learning-based
classifications. Attack classification was achieved using automatic extraction
with the focal loss on the CSE-CIC-IDS2018 dataset is a high-quality classifier
with 98.38% precision, 98.27% sensitivity, and 99.82% specificity.

Yesi Novaria Kunang, Siti Nurmaini, Deris Stiawan, Bhakti Yudho Suprapto

http://journal.uad.ac.id/index.php/TELKOMNIKA

Jun 17, 2021

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