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It is an important and effective approach for the detection of network attacks by means of monitoring and analyzing the running behavior of the program. Traditionally, a program can be characterized by system call issued by it. The call site information of system call, however, is often ignored by many system-call-based detection models. This paper evaluates the influence of the specific information...
Based on Dempster-Shafer (D-S) evidence theory of data fusion technology, a new intrusion detection system (IDS) model with C-SVM classifier is proposed. This model consisted of three SVM classifiers, which sorted out Normal, DoS, U2R, R2L and Probing behaviors from network connections according to basic TCP features, content features and traffic features. Those classified results were obtained through...
Security mechanisms within DBMSs are far from effective to detect and prevent anomalous behavior of applications and intrusions from attackers. In fact, intrusions executed by unauthorized users to explore system vulnerabilities, and malicious database transactions executed by authorized users, both cannot be detected and prevented by typical security mechanisms. In this paper we proposed a database...
This paper presents a new approach to detect attacks from network activities. Network connections were transformed into data points in the predefined feature space. The influence function was designed to quantify the influence of an object and, further, the data field was divided into positive field and negative field according to the source point's category. To perform classification, all the labeled...
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