Optimization of IDS using Filter-Based Feature Selection and Machine Learning Algorithms
Neha Sharma1, Harsh Vardhan Bhandari2, Narendra Singh Yadav3, Harsh Vardhan Jonathan Shroff4

1Neha Sharma* Assistant Professor, Manipal University Jaipur, Rajasthan, India.
2Harsh Vardhan Bhandari B.Tech, Information Technology, Manipal University Jaipur, Rajasthan, India.
3Narendra Singh Yadav, Associate Professor, Manipal University, Jaipur, Rajasthan, India.
4Harsh Vardhan Jonathan Shroff, B. Tech, Information Technology, Manipal University, Jaipur, Rajasthan, India. 

Manuscript received on November 11, 2020. | Revised Manuscript received on November 30, 2020. | Manuscript published on December 10, 2021. | PP: 96-102 | Volume-10 Issue-2, December 2020 | Retrieval Number: 100.1/ijitee.B82781210220| DOI: 10.35940/ijitee.B8278.1210220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Nowadays it is imperative to maintain a high level of security to ensure secure communication of information between various institutions and organizations. With the growing use of internet over the years, the number of attacks over the internet have escalated. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. The aim of an IDS is to monitor the active processes in a network and to detect any deviation from the normal behavior of the system. When it comes to machine learning, optimization is the process of obtaining the maximum accuracy from a model. Optimization is vital for IDSs in order to predict a wide variety of attacks with utmost accuracy. The effectiveness of an IDS is dependent on its ability to correctly predict and classify any anomaly faced by a computer system. During the last two decades, KDD_CUP_99 has been the most widely used data set to evaluate the performance of such systems. In this study, we will apply different Machine Learning techniques on this data set and see which technique yields the best results. 
Keywords: Intrusion detection systems, KDDCUP99, Machine Learning, Classification.