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.
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