Abstract: With the dramatically development of computer network technology in our current society, the threat of cyber intrusion also highly increases. With the increase of usage in computers, criminal activity has also shifted from physical intrusion to cyber intrusion. Intrusion Detection System (IDSs) plays a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. Develop security measures to prevent unapproved access to system resources and data become an urgent problem in the network security field. It is so necessary to discover the intrusion as soon as possible to take effective measures to identify the loopholes and repair the system is called as intrusion detection research. With the incredible expansion of network-based services, network protection and security is more and more significant than ever. IDSs constitute a serious security risk in networking surroundings. Data mining techniques are used to monitor and analyze large amount of network data and classify these network data into anomalous and normal data. Among the different data mining techniques, classification and clustering are the commonly used techniques to build IDS. An effective IDS requires high detection rate, low false alarm rate as well as high accuracy. Our current study presents the review and useful insights into the recent IDS techniques applied for the effective detection of normal and malicious activities in the network.