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An Improved Intrusion Detection System Based on KDD Dataset Using Feature Ranking and Data Sampling

Publication Type : Conference Paper

Publisher : Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020, 2020, pp. 1128–1132, 9182060

Source : Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020, 2020, pp. 1128–1132, 9182060

Campus : Amritapuri

School : School of Computing, School of Engineering

Center : Computer Vision and Robotics, Research & Projects

Department : Computer Science

Verified : Yes

Year : 2020

Abstract : Data security over the network is a prime concern and development of an intrusion detection system (IDS) should be given the highest priority. This manuscript looks forward to develop an intelligent IDS by making use of one of the popular available dataset, the KDD CUP99 dataset. It proposes a framework for building an effective IDS employing feature selection and data sampling techniques. Performance evaluation metrics like Accuracy, Precision, Recall and F-measure are taken into consideration to establish the superiority of the built IDS.

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