基于改进GraphSAGE的网络攻击检测OA

Network attack detection based on deep learning was modeled on Euclidean data and couldn′t capture the structural features within attack data.To address this issue,a network attack detection algo-rithm based on improved graph sample and aggregate(GraphSAGE)was proposed.Firstly,the attack data was initially transformed from a flat structure into a graph structure.Secondly,the GraphSAGE algo-rithm was enhanced in several ways,including the fusion of node and edge features during the message passing phase,consideration of the impact of different source nodes on the target node during the message aggregation phase,and the introduction of residual learning mechanism during the edge embedding gener-ation.The experimental results on two public network attack datasets showed that the overall performance of the proposed algorithm was superior to that of the E-GraphSAGE,LSTM,RNN,and CNN algorithms in binary classification scenarios.And the F1 values of the proposed algorithm were higher than compari-son algorithms on most attack categories in multi classification scenarios.