| Title | LSTM for Anomaly-Based Network Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Althubiti, Sara A., Jones, Eric Marcell, Roy, Kaushik |
| Conference Name | 2018 28th International Telecommunication Networks and Applications Conference (ITNAC) |
| Keywords | anomaly detection, anomaly-based network intrusion detection, CIDDS dataset, composability, computer network security, Computer science, Deep Learning, Intrusion detection, intrusion detection system, learning (artificial intelligence), Long short-term memory, Long-Short-Term Memory, LSTM, Metrics, network intrusion detection, network system, network traffic, pubcrawl, recurrent neural nets, Recurrent neural networks, Resiliency, security of data, Testing, Training |
| Abstract | Due to the massive amount of the network traffic, attackers have a great chance to cause a huge damage to the network system or its users. Intrusion detection plays an important role in ensuring security for the system by detecting the attacks and the malicious activities. In this paper, we utilize CIDDS dataset and apply a deep learning approach, Long-Short-Term Memory (LSTM), to implement intrusion detection system. This research achieves a reasonable accuracy of 0.85. |
| DOI | 10.1109/ATNAC.2018.8615300 |
| Citation Key | althubiti_lstm_2018 |
LSTM for Anomaly-Based Network Intrusion Detection