A Novel Framework for NIDS through Fast kNN Classifier on CICIDS2017 Dataset
K. Vamsi Krishna1, K. Swathi2, B. Basaveswara Rao3
1K.Vamsi Krishna, Ph. D. scholar, Dept. of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, India.
2K. Swathi, Professor, Department of CSE, NRI Institute of Technology, Vijayawada, India.
3B. Basaweswara Rao, Ph.D. scholars Dept. of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3669-3675 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6580018520/2020©BEIESP | DOI: 10.35940/ijrte.E6580.018520
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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: This paper investigates the performance of a Fast k-Nearest Neighbor Classifier (FkNN) for Network Intrusion Detection System (NIDS) on Cloud Environment. For this study Variance Index based Partial Distance Search (VIPDS) kNN  is adopted as an FkNN classifier. A benchmark dataset CICIDS2017 is considered for the evaluation process because it is a 78 featured dataset with most updated cloud related attacks. To achieve this objective a frame work is proposed for implementing FkNN and compared with kNN classifier by considering two performance measures Accuracy and computational time. This study explores the gain in the computational time without compromising the Accuracy while using FkNN instead of kNN over a large featured dataset. The conclusions are drawn as per the results obtained from the experiments conducted on CICIDS2017 dataset.
Keywords: Fast kNN Classifier, Network Intrusion Detection System, Variance Indexing, and Cloud.
Scope of the Article: Middleware for service based systems.