Design of Bi-LSTM Algorithm for Network Security Intrusion Detection
Traditional network security intrusion detection systems often have high false alarm rate and low accuracy when fa-cing complex and ever-changing network attacks.In order to improve this situation,this study combines the advantages of re-current neural network(RNN)and convolutional neural network(CNN),and improves the long short-term memory(LSTM)model to design a bidirectional long short-term memory(Bi-LSTM)algorithm for network security intrusion detection.After experimental verification,this algorithm achieves significant results in network security intrusion detection.Under five different types of attack methods,the accuracy of the CNN-Bi-LSTM model reaches 94.8％,90.2％,96％,90.5％and 93.7％,re-spectively,and the false alarm rates are 5.98％,7.2％,5.23％,6.84％and 6.49％,respectively.These data indicate that the designed Bi-LSTM has high accuracy and low false alarm rate in network security intrusion detection,so it has certain ap-plication value and research value.
deep learningconvolutional neural networkintrusion detectionrecurrent neural networklong short-term memo-ry network
万方数据
维普
