计算机工程与应用2019，Vol.55Issue(5)：76-82,7.DOI:10.3778/j.issn.1002-8331.1712-0080
基于LSTM的动态图模型异常检测算法研究
Research on Algorithm of Dynamic Graph Anomaly Detection Based on LSTM
摘要
Abstract
Traditional anomaly detection method most is based on content features, with the increase of attack technology, this kind of method is easy to be circumvented. Therefore, graph mining technology has become a hot topic in academic research both at home and abroad. In order to improve the accuracy of anomaly detection, a dynamic graph anomaly detec-tion algorithm based on long-short term memory network is proposed. First, by analyzing the change characteristics of dynamic graph, it extracts two kinds of characteristics of Egonet:graph structure distance and edit distance, which efficiently express the structural change of dynamic graph. Secondly, the model is trained by the time series classification algorithm based on LSTM. Finally, the captured network flow is used to detect intrusion, and test the dynamic graph topology of more than 60 thousand nodes and 3 million edges. The final experimental results show that the algorithm has a higher accuracy and recall, and can effectively detect network intrusion events.关键词
异常检测/图挖掘/时间序列/长短时记忆(LSTM)Key words
anomaly detection/ graph mining/ time series/ Long Short-Term Memory(LSTM)分类
信息技术与安全科学引用本文复制引用
王凯,陈丹伟..基于LSTM的动态图模型异常检测算法研究[J].计算机工程与应用,2019,55(5):76-82,7.基金项目
国家自然科学基金(No.61572530) （No.61572530）
赛尔网络下一代互联网技术创新项目(No.NGII20160113). （No.NGII20160113）
