Detecting App-DDoS Attacks Based on Maximal Frequent Sequential Pattern Mining

In order to describe the users access behavior dynamically, efficiently and accurately, a novel detection model for Application-layer Distributed Denial of Service (App-DDoS) attack based on maximal frequent sequential pattern mining is proposed, named App-DDoS Detection Algorithm based on Maximal Frequent Sequential Pattern mining (ADA_MFSP). After mining maximal frequent sequential patterns of trained and detected Web Access Sequence Database (WASD), the model introduces sequence alignment, view time and request circulation abnormality to describe the behaviour of App-DDoS attacks, finally achieves the purpose of attack detection. It is proved with experiments that the ADA_MFSP model can not only detect kinds of App-DDoS attacks, but also has good detection sensitivity.