Version 1
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 24 July 2024 (07:00:20 CEST)
How to cite:
Efendi, R.; Wahyono, T.; Widiasari, I. R. LSTM SMOTE: An Effective Strategies for DDoS Detection in Imbalanced Network Environments. Preprints2024, 2024071825. https://doi.org/10.20944/preprints202407.1825.v1
Efendi, R.; Wahyono, T.; Widiasari, I. R. LSTM SMOTE: An Effective Strategies for DDoS Detection in Imbalanced Network Environments. Preprints 2024, 2024071825. https://doi.org/10.20944/preprints202407.1825.v1
Efendi, R.; Wahyono, T.; Widiasari, I. R. LSTM SMOTE: An Effective Strategies for DDoS Detection in Imbalanced Network Environments. Preprints2024, 2024071825. https://doi.org/10.20944/preprints202407.1825.v1
APA Style
Efendi, R., Wahyono, T., & Widiasari, I. R. (2024). LSTM SMOTE: An Effective Strategies for DDoS Detection in Imbalanced Network Environments. Preprints. https://doi.org/10.20944/preprints202407.1825.v1
Chicago/Turabian Style
Efendi, R., Teguh Wahyono and Indrastanti Ratna Widiasari. 2024 "LSTM SMOTE: An Effective Strategies for DDoS Detection in Imbalanced Network Environments" Preprints. https://doi.org/10.20944/preprints202407.1825.v1
Abstract
In detecting DDoS, deep learning faces challenges and difficulties such as high computational demands, long training times, and complex model interpretation. This research focuses on overcoming these challenges by proposing an effective strategy for detecting DDoS attacks in unbalanced network environments. This research uses SMOTE to increase the class distribution of the data set by allowing models using LSTM to learn time anomalies effectively when DDoS attacks occur. The experiments carried out have shown significant improvement in the performance of the LSTM model when integrated with SMOTE. These include validation loss results of 0.048 for LSTM SMOTE and 0.1943 for LSTM without SMOTE, accuracy of 99.50 and 97.50. Apart from that, there was an increase in the f1 score from 93.4% to 98.3%. In this research, it is proven that SMOTE can be used as an effective strategy to improve model performance in detecting DDoS attacks on heterogeneous networks, as well as increasing model robustness and reliability.
Keywords
Imbalanced Network; DDoS; SMOTE; LSTM
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
