| Title | Intrusion Prediction using Long Short-Term Memory Deep Learning with UNSW-NB15 |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kim, Seongsoo, Chen, Lei, Kim, Jongyeop |
| Conference Name | 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD) |
| Keywords | anomaly-based IDS, Big Data, big data security metrics, cloud computing, Data Science, Deep Learning, LSTM, machine learning, Measurement, Predictive models, pubcrawl, resilience, Resiliency, RMSE, Scalability, Training data |
| Abstract | This study shows the effectiveness of anomaly-based IDS using long short-term memory(LSTM) based on the newly developed dataset called UNSW-NB15 while considering root mean square error and mean absolute error as evaluation metrics for accuracy. For each attack, 80% and 90% of samples were used as LSTM inputs and trained this model while increasing epoch values. Furthermore, this model has predicted attack points by applying test data and produced possible attack points for each attack at the 3rd time frame against the actual attack point. However, in the case of an Exploit attack, the consecutive overlapping attacks happen, there was ambiguity in the interpretation of the numerical values calculated by the LSTM. We presented a methodology for training data with binary values using LSTM and evaluation with RMSE metrics throughout this study. |
| DOI | 10.1109/BCD51206.2021.9581420 |
| Citation Key | kim_intrusion_2021 |
Intrusion Prediction using Long Short-Term Memory Deep Learning with UNSW-NB15