Identifikasi Serangan Low-Rate DDOS Berbasis Deep Learning

Wahyuni Wahyuni; Pitrasacha Adytia

doi:10.51967/tanesa.v23i2.1737

Authors

Wahyuni Teknik Informatika, STMIK Widya Cipta Dharma
Pitrasacha Adytia Sistem Informasi, STMIK Widya Cipta Dharma, Samarinda

DOI:

https://doi.org/10.51967/tanesa.v23i2.1737

Keywords:

Low-Rate DDOS, Deep Learning, RNN LSTM.

Abstract

LowRate DDoS (LDDoS) is a variation of DDoS attack that sends fewer packets than conventional DDoS attacks. However, by sending a smaller number of packets and using a unique attack period, low-rate DDoS is very effective in reducing the quality of an internet network-based service due to full access. On the other hand, the low-rate DDoS with its nature also makes it difficult to detect because it looks more mixed with normal user access. The Deep Learning model that will be used in this research is the RNN LSTM (Long Short Term Memory) model. LSTM is a neural network architecture which is good enough to process sequential data. This model is better than the simple RNN model. The research method is adapted to the SKKNI No. 299 of 2020. However, this research will be carried out until the model development stage, namely the evaluation model. From the results of the research that has been done, it can be concluded that the RNN LSTM model can be used to classify low-rate DDOS attacks using feature selection. The accuracy of the training data on the validation data is around 98% and after visualizing the data for accuracy and loss, it can be concluded that the model is quite good, aka there is no underfitting or overfitting. While the accuracy obtained for testing data is 0.97%.

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