• Ganteela Raghu Bhaskar

An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. 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 Naive Bayes based, Decision Tree based, Random Forest based and support vector machine based, systems for intrusion detection.

Classification; Features selection; Intrusion detection system; NSL-KDD; Network; machine learning

"BUILDING A SYSTEM TO DETECT THE INTRUSIONS OVER NETWORK USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.225-232, June 2019, Available :http://www.jetir.org/papers/JETIR1906T27.pdf

"BUILDING A SYSTEM TO DETECT THE INTRUSIONS OVER NETWORK USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp225-232, June 2019, Available at : http://www.jetir.org/papers/JETIR1906T27.pdf