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@article{144937,
        author = {Gurunadham R and Madhu Nakerekanti},
        title = {Real Time Intrusion Detection Systems using Data Mining Practices },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {6},
        pages = {174-179},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144937},
        abstract = {Due to the widespread proliferation of computer networks, attacks on computer systems are increasing day by day. Preventive measures can stop these attacks to some extent, but they are not very effective due to various reasons. This lead to the development of intrusion detection as a second line of defense. 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 focused on data mining techniques that are being used for such purposes. We debate on the advantages and disadvantages of these techniques. Finally we present a new idea on how data mining can aid IDSs in real time. In this paper, we present an overview of real time data mining-based intrusion detection system (IDSs). We focus on issues related to deploying a data mining -based IDS in a real time environment. New intelligent Intrusion Detection Systems (IDSs) are based on sophisticated algorithms rather than current signature-base detections are in demand. In this paper, we propose a new real time data-mining based technique for intrusion detection using an ensemble of binary classifiers with feature selection and multiboosting simultaneously.},
        keywords = {Data Mining , DOS attack, Feature Selection ,Intrusion Detection Systems, Multiboosting, Network Security, Real time IDS},
        month = {},
        }