Research Paper | Computer Science & Engineering | India | Volume 7 Issue 10, October 2018
Network Intrusion Detection Using Machine Learning Techniques
Sayi Sruthi.K | Liston Deva Glindis
Abstract: Network traffic anomaly indicates a possible intrusion in the network and therefore anomaly detection is important to detect and prevent the security attacks. The early research works in this area and commercially available in Intrusion Detection Systems (IDS) they are mostly signature-based. The problem of signature based method is that the database signature needs to be updated as new attack signatures become available and therefore it is not suitable for the real-time network anomaly detection. The recent trend in anomaly detection is based on machine learning classification techniques. We apply seven different machine learning techniques with information entropy calculation to Kyoto 2006+ data set and evaluate the performance of these techniques. Our findings show that, for this particular data set, most machine learning techniques provide higher than 90 % precision, recall and accuracy. However, using area under the Receiver Operating Curve (ROC) metric, we find that Radial Basis Function (RBF) performs the best among the seven algorithms studied in this work.
Keywords: KYOTO 2006+ DATA SET, ROC METRICS, RBF ALGORITHM, ACCURACY, PRECISION, RECALL
Edition: Volume 7 Issue 10, October 2018,
Pages: 920 - 924
How to Cite this Article?
Sayi Sruthi.K, Liston Deva Glindis, "Network Intrusion Detection Using Machine Learning Techniques", International Journal of Science and Research (IJSR), Volume 7 Issue 10, October 2018, pp. 920-924, https://www.ijsr.net/get_abstract.php?paper_id=ART20192121
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