A distributed intrusion detection system based on CNN-GRU in cloud environment

Abstract

In the era of big data, the network is always full of massive data. Cloud computing provides huge technical support for processing massive data. The cloud environment stores a large number of important information of individuals, enterprises and even countries. It has high commercial value, so it has become the primary target of many network attacks. Therefore, it is necessary to monitor the traffic in the cloud environment in real time and block abnormal traffic in time to ensure a safe and stable network environment for users. The existing intrusion detection systems can be divided into software systems and hardware systems, which are deployed in the backbone network in the network environment for real-time traffic detection. It is difficult to meet the traffic detection of multi branches and massive data in the cloud environment. This paper combines the deep neural network model with Hadoop framework, and proposes a distributed intrusion detection system model based on CNN-GRU. The deep neural network model is deployed in multiple nodes in the cloud environment, and the data is stored through HDFS and mapped and integrated by MapReduce method, so as to realize the intrusion detection of multi node parallel cloud environment. Finally, through the open source intrusion detection data set, the experimental results prove the effectiveness of the proposed method.