PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion Detection)
نویسندگان
چکیده
منابع مشابه
Improving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering
Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...
متن کاملPNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-Study in Network Intrusion
Learning classifier models is an important problem in data mining. Observations from the real world are often recorded as a set of records, each characterized by multiple attributes. Associated with each record is a categorical attribute called class. Given a training set of records with known class labels, the problem is to learn a model for the class in terms of other attributes. The goal is ...
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Information security and Intrusion Detection System (IDS) plays a critical role in the Internet. IDS is an essential tool for detecting different kinds of attacks in a network and maintaining data integrity, confidentiality and system availability against possible threats. In this paper, a hybrid approach towards achieving high performance is proposed. In fact, the important goal of this paper ...
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A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate t...
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