| Title | RNN-based Prediction for Network Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Park, S. H., Park, H. J., Choi, Y. |
| Conference Name | 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
| Date Published | feb |
| Keywords | anomaly detection, anomaly detection methods, composability, cosine similarity, data mining, data mining regression techniques, Data models, Euclidean distance, IDS, industrial IoT environments, Internet of Things, Intrusion detection, LSTM, LSTM model, Metrics, N-gram, network intrusion detection, prediction model, Predictive models, production engineering computing, pubcrawl, recurrent neural nets, regression analysis, resilience, Resiliency, RNN, RNN-based prediction, scoring function, security of data, sliding window |
| Abstract | We investigate a prediction model using RNN for network intrusion detection in industrial IoT environments. For intrusion detection, we use anomaly detection methods that estimate the next packet, measure and score the distance measurement in real packets to distinguish whether it is a normal packet or an abnormal packet. When the packet was learned in the LSTM model, two-gram and sliding window of N-gram showed the best performance in terms of errors and the performance of the LSTM model was the highest compared with other data mining regression techniques. Finally, cosine similarity was used as a scoring function, and anomaly detection was performed by setting a boundary for cosine similarity that consider as normal packet. |
| DOI | 10.1109/ICAIIC48513.2020.9065249 |
| Citation Key | park_rnn-based_2020 |
RNN-based Prediction for Network Intrusion Detection