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.
KEYWORDS: Switches, Switching, Signal processing, Remote sensing, Associative arrays, Data communications, Mobile communications, Signal analyzers, Motion analysis, Manganese
The idea of SDN provides a new way to solve the problems faced by mobility management of network layer in traditional networks. However, the existing mobility management schemes in SDN still face problems such as large network overhead and prolonged switching time in the process of user mobility. In this paper, the mobility management mechanism of network layer supporting IPv6 in SDN is studied. Firstly, by analyzing and designing two key technologies of mobility awareness and mobility handover, and combining with the position prediction theory, an IPv6 mobility management mechanism supporting pre-handover in SDN is proposed. Secondly, according to the result of position prediction, this mechanism calculates and issues the switching path runoff table before the movement, which reduces the signaling overhead and switching delay of the mobile node in the process of movement. Finally, according to the performance evaluation and analysis method, the signaling cost and switching delay of OPMIPv6-C scheme which is also implemented based on SDN are compared.
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