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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Internet of Things (IoT) is a trending domain and has acquired much interest for various kinds of civilian applications. The purpose of IoT is to make objects accessible and interconnected via internet. Hence, security to IoT devices is a major issue because devices connected to the IoT network are resource‐constrained. In IoT, the nodes exchange information using insecure internet, which makes the network exposed to different attacks. This article proposes a new intrusion detection strategy, namely, Taylor‐spider monkey optimization‐based deep belief network (Taylor‐SMO‐based DBN). The KDD features and the trust factors are employed for intrusion detection. The KDD features are subjected to the classification, which is progressed using a newly devised optimization algorithm, namely, Taylor‐spider monkey optimization (Taylor‐SMO)‐based DBN. The proposed Taylor‐SMO algorithm is designed by integrating the Taylor series and spider monkey optimization (SMO) algorithm and is employed to train the deep belief network (DBN) to achieve accurate intrusion detection. The proposed Taylor‐SMO‐based DBN outperformed other methods with maximal accuracy of 90%, false alarm rate of10%, precision of 90%, and recall of 92%, respectively.