Using Long-Short-Term Memory Based Convolutional Neural Networks for Network Intrusion Detection

The quantity of internet use has grown dramatically in the last decade. Internet is almost available in every human activity. However, there are some critical obstacles behind this massive development. Security becomes the hottest issue among the researchers. In this study, we focus on intrusion detection system (IDS) which is one of the solutions for security problems on network administration. Since intrusion detection system is a kind of classifier machine, it is allowed to engage with machine learning schemes. Related to this reason, the number of studies related to utilizing machine learning schemes for intrusion detection system has been increased recently. In this study, we use NSL-KDD dataset as the benchmark. Even though machine learning schemes perform well on intrusion detection, the obtained result on NSL-KDD dataset is not satisfied enough. On the other hand, deep learning offers the solution to overcome this issue. We propose two deep learning models which are long-short-term memory only (LSTM-only) and the combination of convolutional neural networks and LSTM (CNN-LSTM) for intrusion detection system. Both proposed methods achieve better accuracy than that of the existing method which uses recurrent neural networks (RNN).