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Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kim, Jihyun Kim, Jaehyun Thu, Huong Le Thi Kim, Howon |
| Copyright Year | 2016 |
| Abstract | Due to the advance of information and communication techniques, sharing information through online has been increased. And this leads to creating the new added value. As a result, various online services were created. However, as increasing connection points to the internet, the threats of cyber security have also been increasing. Intrusion detection system(IDS) is one of the important security issues today. In this paper, we construct an IDS model with deep learning approach. We apply Long Short Term Memory(LSTM) architecture to a Recurrent Neural Network(RNN) and train the IDS model using KDD Cup 1999 dataset. Through the performance test, we confirm that the deep learning approach is effective for IDS. |
| Starting Page | 1 |
| Ending Page | 5 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/PlatCon.2016.7456805 |
| Alternate Webpage(s) | http://www.covert.io/research-papers/deep-learning-security/Long%20Short%20Term%20Memory%20Recurrent%20Neural%20Network%20Classifier%20for%20Intrusion%20Detection.pdf |
| Alternate Webpage(s) | https://doi.org/10.1109/PlatCon.2016.7456805 |
| Journal | 2016 International Conference on Platform Technology and Service (PlatCon) |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
