• Tanuku Durga Naga Lakshmi
• RAKESH KUPPILI

Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network. High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult. Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process. Most techniques used in today's IDS are not able to deal with the dynamic and complex nature of cyber-attacks on computer networks. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. Using a set of benchmark data from a KDD (knowledge discovery and data mining) competition designed by DARPA, we demonstrate that efficient and accurate classifiers can be built to detect intrusions. We compare the performance of Decision Tree based, Random Forest based and support vector machine based, systems for intrusion detection.

"An Implementation of Intrusion Detection System Using Machine Learning Algorithms on Big Data Environment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.521-528, March-2019, Available :http://www.jetir.org/papers/JETIR1903A80.pdf

"An Implementation of Intrusion Detection System Using Machine Learning Algorithms on Big Data Environment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp521-528, March-2019, Available at : http://www.jetir.org/papers/JETIR1903A80.pdf