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Research Paper | Computer Science & Engineering | India | Volume 4 Issue 4, April 2015
A Hybrid System Using Genetic Algorithm for Anomaly Intrusion Detection
Arpitha J | Nagaraj Naik [3]
Abstract: This paper proposes a new approach to design the system using a hybrid of misuse and anomaly detection for training of normal and attack packets respectively. The hybrid intrusion detection system combines the K-means and the genetic algorithm for anomaly detection. This algorithm operates on the KDD-99 Data set, this data set is used worldwide for evaluating the performance of different intrusion detection systems. The system can detect the intrusions and further classify them into four categories Denial of Service (DoS), U2R (User to Root), R2L (Remote to Local), and probe. The main goal is to reduce the false alarm rate of IDS.
Keywords: Clustering, Classification, K-Means, Genetic Algorithm, Detection rate, False alarm rate, Intrusion detection, data mining, KDD cup 99
Edition: Volume 4 Issue 4, April 2015,
Pages: 3054 - 3057
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Student Project, Computer Science & Engineering, India, Volume 11 Issue 6, June 2022
Pages: 1875 - 1880Microclustering with Outlier Detection for DADC
Aswathy Priya M.
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Analysis Study Research Paper, Computer Science & Engineering, India, Volume 12 Issue 11, November 2023
Pages: 1840 - 1846Analysis of Placement for Electronics and Communication Engineering Students using Multiple Clustering
Dr. Dola Sanjay S