A Network Intrusion Security Detection Method Using BiLSTM-CNN in Big Data Environment
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The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetectiontargets effectively, which lead to low detection accuracy. In this study, a NIDS method which basedon a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDSmodel is constructed in two stages. Feature reduction and anomaly probability output are used at the core ofthe two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer anda characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducingbidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to samplethe required features according to different sampling rules, which promotes the overall performance of theNIDS model. The proposed NIDS method and three other methods are compared, and it is broken down underthe conditions of the two databases through simulation experiments. The results demonstrate that the proposedmodel is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1-score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm issignificant for improving the accuracy of NIDS.
Abstract
The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetectiontargets effectively, which lead to low detection accuracy. In this study, a NIDS method which basedon a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDSmodel is constructed in two stages. Feature reduction and anomaly probability output are used at the core ofthe two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer anda characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducingbidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to samplethe required features according to different sampling rules, which promotes the overall performance of theNIDS model. The proposed NIDS method and three other methods are compared, and it is broken down underthe conditions of the two databases through simulation experiments. The results demonstrate that the proposedmodel is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1-score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm issignificant for improving the accuracy of NIDS.
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