TY  - JOUR
T1  - Survey Paper on Various Hybrid and Anomaly based
Network Intrusion Detection System
AU - Baby, J. Josemila AU - Jeba, J.R. 
JO  - Research Journal of Applied Sciences
VL  - 12
IS  - 3
SP  - 304
EP  - 310
PY  - 2017
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2017.304.310
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2017.304.310
KW  - Review
KW  -intrusion detection
KW  -IDS
KW  -data mining
KW  -anomaly detection
KW  -alarm rate
AB  - 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.
ER  -