The relentless advancement of Generative Adversarial Network (GAN) technology has stimulated research interest in exploiting its unique properties within the realm of network security. In conjunction with the maturity and growth of artificial intelligence, employing AI technology for SQL injection detection has evolved into an unalterable trend. This study introduces the application of an Improved Evolutionary Generative Adversarial Network (IE-GAN) for the synthesis of SQL injection attacks, thereby facilitating the generation of realistic SQL injection attack examples. Given the considerable threat posed by SQL injection attacks to the security of web applications, the demand for robust and efficient detection mechanisms has escalated significantly. Earlier research has made considerable progress in incorporating GANs in the field of cybersecurity.Our study builds upon these achievements, utilizing a comprehensive dataset that includes both benign and malicious SQL queries. To ensure the efficiency of model training, these data are subjected to intricate preprocessing and vectorization processes, which are discussed in the paper in detail.

Load balancing, which redistributes dynamic workloads across computing nodes within cloud to improve resource utilization, is one of the main challenges in cloud computing system. Most existing rule-based load balancing algorithms failed to effectively fuse load data of multi-class system resources. The strategies they used for balancing loads were far from optimum since these methods were essentially performed in a combined way according to load state. In this work, a fuzzy clustering method with feature weight preferences is presented to overcome the load balancing problem for multi-class system resources and it can achieve an optimal balancing solution by load data fusion. Feature weight preferences are put forward to establish the relationship between prior knowledge of specific cloud scenario and load balancing procedure. Extensive experiments demonstrate that the proposed method can effectively balance loads consisting of multi-class system resources.

Cloud storage allows users to store their data in a remote server to get rid of expensive local storage and management costs and then access data of interest anytime anywhere. A number of solutions have been proposed to tackle the verification of remote data integrity and retrievability in cloud storage systems. Most of existing schemes, however, do not support efficient data dynamics and/or suffer from security vulnerabilities when involving dynamic data operations. In this paper, we propose a dynamic proof of retrievability scheme supporting public auditability and communication-efficient recovery from data corruptions. To this end, we split up the data into data blocks and encode each data block individually using outer code and inner code before outsourcing so that i) an update inside any data block only affects a few codeword symbols and ii) communication-efficient data repair for a breakdown server can be achieved and communication overhead for small data corruptions within a server can be eliminated. Based on the encoded data blocks, we utilize rb23Tree to enforce the data sequence for dynamic operations, preventing the cloud service provider from manipulating data block to pass the integrity check in the dynamic scenario. Formal security analysis and extensive experimental evaluations are conducted, showing that the proposed scheme is practical for use in cloud storage systems.

Security of computer architecture is basis of trusted system. For the sake of compromising to commerce,trusted computing platform's product is really not the best secure architecture. It is short of supporting trust spread because of chain trust relationship. So we put forward a specific trust model, and then advance a secure and reconfigurable architecture distinguishing from traditional architectures. The model can give prominence to trusted root and reduce the loss of trust after trust chain had transferred. We also design out new architecture according to our model. The prototype system validates that new architecture can effectively resolve trust expansion.

Secure bootstrap usually use integrity measurement scheme to verify code or data before they are loaded into memory. As to traditional PC (personal computer) architecture, this method is hard to protect security of system software and application especially.This paper researches the different components running on system and divides them into two kind TCB (trusted computing base), i.e. HTCB (hardwareTCB) and STCB (software TCB). Combing with technologies such as VM (virtual machine), integrity measurement and sealed storage, prototype lets STCB bind with HTCB so that trusted chain can be extended from hardware layer to system software layer. Lastly,detailed implement ways and means for prototype are given.