Unsw-Nb15 Dataset and Machine Learning Based Intrusion Detection Systems
Avinash R. Sonule1, Mukesh Kalla2, Amit Jain3, D. S. Chouhan4
1Avinash R. Sonule*, Department of Computer Science & Engineering, Sir Padampat Singhania University(SPSU), Udaipur-313601, Rajasthan, India
2Mukesh Kalla, Department of Computer Science &Engineering, Sir Padampat Singhania University(SPSU), Udaipur-313601, Rajasthan, India
3Amit Jain, Department of Computer Science &Engineering, Sir Padampat Singhania University(SPSU), Udaipur-313601, Rajasthan, India
4D. S. Chouhan, Department of Computer Science &Engineering, Sir Padampat Singhania University(SPSU), Udaipur-313601, Rajasthan, India
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2638-2648 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5809029320/2020©BEIESP | DOI: 10.35940/ijeat.C5809.029320
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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: The network attacks become the most important security problems in the today’s world. There is a high increase in use of computers, mobiles, sensors,IoTs in networks, Big Data, Web Application/Server,Clouds and other computing resources. With the high increase in network traffic, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. Machine learning algorithms intend to detect anomalies using supervised and unsupervised approaches.Both the detection techniques have been implemented using IDS datasets like DARPA98, KDDCUP99, NSL-KDD, ISCX, ISOT.UNSW-NB15 is the latest dataset. This data set contains nine different modern attack types and wide varieties of real normal activities. In this paper, a detailed survey of various machine learning based techniques applied on UNSW-NB15 data set have been carried out and suggested thatUNSW-NB15 is more complex than other datasets and is assumed as a new benchmark data set for evaluating NIDSs.
Keywords: Intrusion Detection System, UNSW-NB15dataset, Network Intrusion Detection System (NIDS).