Comparison of Data Mining Techniques for Building Network Intrusion Detection Models
نویسندگان
چکیده
منابع مشابه
A Data Mining Framework for Building Intrusion Detection Models
There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are constructed by manual encoding of expert knowledge, changes to IDSs are expensive and slow. In this paper, we describe a data mining framework for adaptively building Intrusion Detection (ID) models. The central idea is to util...
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In Information Security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. Intrusion detection does not, in general, include prevention of intrusions. In this paper, we are mostly focused on data mining techniques that are being used for such purposes. We debate on the advantages and disadvantages of thes...
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Network intrusion detection systems have become a standard component in security infrastructures. Unfortunately, current systems are poor at detecting novel attacks without an unacceptable level of false alarms. We propose that the solution to this problem is the application of an ensemble of data mining techniques which can be applied to network connection data in an offline environment, augme...
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Data mining techniques have been successfully applied in many fields including marketing, manufacturing, process control, fraud detection and network management. In recent years, data mining-based Intrusion Detection Systems (IDS) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. In data mining based intrusion detect...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2016
ISSN: 0975-8887
DOI: 10.5120/ijca2016909840