A Semantic Parsing Based Lstm Model For Intrusion Detection
NEURAL INFORMATION PROCESSING (ICONIP 2018), PT IV(2018)
摘要
Nowadays, with the great success of deep learning technology, using deep learning method to solve information security issues has become a study hot spot. Although some literal works have tried to solve intrusion detection problem via recurrent neural network, these methods do not give a detailed framework and specific data processing progress. We propose a novel semantic parsing based Long Short-Term Memory (LSTM) network framework in this paper. The proposed method uses the semantic representations of network data. The novel conversion process of various forms of network data to semantic description is given in detail. Experiments on NSL_KDD data sets show our proposed model outperforms most of the standard classifier. Results show that the semantic description has reserved information of the data and our semantic parsing based LSTM model provides a novel way to solve anomaly detection.
更多查看译文
关键词
Anomaly detection, Semantic parsing, LSTM, NSL_KDD
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络