Establishing a complete information security policy is the most important step to solve the problem of information security and the basis for the entire information security system. Using intrusion detection technology to identify the source of threats and adjusting security policy is an effective operation of network protection. Trained BP neural network model is usually adopted as detector, but because of defects of weights training algorithm of BPNN, the weights always fall into local minima area. In order to address this problem, we propose a shorter training time and can achieve a superior detection rate than BPNN.
Abstract. Project development in a power enterprise always needs to authorize external devices access to the enterprise intranet for testing. In order to avoid an external device with a virus and pose a security risk to the power information system, external devices should have strict security assessment before access the enterprise intranet. But after the security assessment, the device user still be possible to change the platform configuration. Remote attestation is one of important measures when two sides need to communicate. It is concernful to attest the remote platform is trusty but not revealing the any private information of the platform. For this reason, we designed a novel remote anonymous attestation protocol based on TCM. The proposed protocol does not need extra zero knowledge proof and the involvement of the third trusted party and the composite signature scheme is proved secure against existential forgery on adaptively chosen message. So this protocol has better security and execution property.
In order to protect vital data in today's internet environment and prevent misuse, especially insider abuse by valid users, we propose a novel two-step detecting approach to distinguish potential misuse behaviour (namely anomalous user behaviour) from normal behaviour. First, we capture the access patterns of users by using association rules. Then, based on the patterns and users' sequential behaviour, we try to deter anomalous user behaviour by leveraging the logistic regression model. Experimental results on real dataset indicate that our method can get a better result and outperform two state-of-the-art method. The proposed two-step detecting approach can effectively detect anomalous user behaviour from the log data generated by operation and maintenance staffs.
This paper researches the method of SQL injection attack detection and the principle of static analysis scanning, and presents a Java source-code SQL injection attack detection algorithm. The detection algorithm includes these steps: lexical analysis of source code, parsing of source code, constructing abstract syntax tree of source code, defining rules, abstract syntax tree traversal, tracking problems, detecting possible paths of SQL injection attack etc. Test results show the proposed detection algorithm in this paper performs perfectly and has higher recognition rate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.