Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME

Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on March 26-27, 2018

6 Pages Posted: 9 May 2018

See all articles by Ashi Sinha

Ashi Sinha

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

Somika Rastogi

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering

Gagandeep Kaur

JIIT

Date Written: April 28, 2018

Abstract

The growth of Internet has led to birth of new and strong network based attacks. These attacks cannot be completely detected with traditional supervised intrusion detection techniques. Machine Learning based on unsupervised learning is slowly taking it’s place. Moreover the rate of traffic has increased tremendously. It is important to work on tools and techniques that can support large volumes of data. It is under these scenarios that we have worked on KNIME to understand it’s scope in handling big datasets of more than 20 Gbs.

Suggested Citation

Sinha, Ashi and Rastogi, Somika and Kaur, Gagandeep, Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME (April 28, 2018). Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on March 26-27, 2018. Available at SSRN: https://ssrn.com/abstract=3170295 or http://dx.doi.org/10.2139/ssrn.3170295

Ashi Sinha (Contact Author)

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering ( email )

A-10
Sector-62
Noida, Uttar Pradesh 201307
India

Somika Rastogi

Jaypee Institute of Information Technology (JIIT) - Department of Computer Science and Engineering ( email )

A-10
Sector-62
Noida, Uttar Pradesh 201307
India

Gagandeep Kaur

JIIT ( email )

Uttar Pradesh
India

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