Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
This work addresses forecasting DDoS attacks for cybersecurity, but it appears incremental as it builds on existing methods with newer data.
The paper tackles forecasting future DDoS attacks using deep learning models, specifically an LSTM, to enable better mitigation planning based on updated datasets.
This paper forecasts future Distributed Denial of Service (DDoS) attacks using deep learning models. Although several studies address forecasting DDoS attacks, they remain relatively limited compared to detection-focused research. By studying the current trends and forecasting based on newer and updated datasets, mitigation plans against the attacks can be planned and formulated. The methodology used in this research work conforms to the Cross Industry Standard Process for Data Mining (CRISP-DM) model.