This paper is published in Volume-4, Issue-2, 2018
Area
Information Technology
Author
Radhika Gupta
Org/Univ
Maharaja Agrasen Institute of Technology, Rohini, Delhi, India
Keywords
KSQL, Anomaly Detection, Decision Tree, Logistic Regression, K-nearest Neighbour, K-means, Network Intrusion Detection, KSQL, Accuracy
Citations
IEEE
Radhika Gupta. Anomaly Detection and Data Classification using KSQL, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Radhika Gupta (2018). Anomaly Detection and Data Classification using KSQL. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.
MLA
Radhika Gupta. "Anomaly Detection and Data Classification using KSQL." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.
Radhika Gupta. Anomaly Detection and Data Classification using KSQL, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Radhika Gupta (2018). Anomaly Detection and Data Classification using KSQL. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.
MLA
Radhika Gupta. "Anomaly Detection and Data Classification using KSQL." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.
Abstract
We are going to use KSQL for data analysis. KSQL is an open source streaming SQL engine for Apache Kafka. It allows for identifying patterns or anomalies in real-time data. We will attempt to do classification on a publicly available set of data. We will build models using the training data. We can then compare them by running test data through them. The dataset that is going to be used is the KDDCUP99 Network Intrusion dataset.
