Research On Key Technologies Of Cloud Security Immune System Based On Behavioral Analysis

Along with the advent of big data era and the development of IT technology, computer virus has also been evolving swiftly and the safety of cloud data has become a key issue. At present, the main security threats include intrusion attacks, network worm and the spread of Trojans through web pages, etc. The traditional feature code technology can identify the vast majority of known threats, however, the hysteretic nature of feature code technology makes the featured antivirus method lag behind the generation of virus, which cannot effectively detect the unknown virus without features in the virus reservoir. Under such circumstances, only original feature code recognition method already cannot meet the requirements of network.At the same time, with the rapid development of cloud computing, it is also faced with how to realize the security of user data in the massive nodes and massive information processing requirements under the premise of the challenge. When the cloud of traditional security technology in response to these problems, often reach the ideal time and space efficiency, accuracy and intelligence of the poor, and occupy more bandwidth.In order to better guarantee the safety of data run under the environment of cloud, cloud security immune system (CSIS) based on the theory of human immunity and behavioral analysis technology has been put forward by taking operational mechanism of human immune system as theoretical basis, using its functional characteristics for reference and combining immune system of computer with characteristics of cloud computing. Operational mechanism of CSIS and nine related algorithms have been designed. Moreover, its characteristics have been analyzed. The experiment indicates that CSIS enjoys a good recognition rate in abnormal behavior characteristics and can preferably guarantee the operational safety under the cloud environment.In order to further improve the performance of the system, behavior analysis technology is introduced to the CSIS, and behavior analysis of detection model is proposed and with self set and non-self set combination, to further improve the system recognition accuracy. The experimental results show that the CSIS based on behavioral analysis can greatly reduce the time required for the system which ensure the detection rate, and further improve the performance of the whole system.