TY  - JOUR
T1  - Comparative Study of Multiple Intrusion Detection Systems
AU - Saravanan, S. AU - Ramakrishnan, M. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 18
SP  - 3605
EP  - 3610
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3605.3610
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3605.3610
KW  - Ad hoc network
KW  -multiple intrusion detection
KW  -neighborhood outlier factor
KW  -anomalies
KW  -approaches
AB  - Network security is of paramount importance in the present data communication environment.
Network security is a critical problem because a single attack can inflict catastrophic damages to the computers
and network systems. The various hackers and intruders can create multiple successful attempts to cause the
crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to
detect and prevent these threats are emerging together with the secured system evolution. The best solution
to solve this issue is Intrusion Detection Systems (IDS). The important function of Intrusion Detection System
(IDS) is to secure the resources from threats. In this study, we have presented a brief study about
characteristics of ad hoc network, how they are problematic in ad hoc network security, attacks in ad hoc
network and a description of some existing intrusion detection system. We have also justified why multiple
intrusion detection is much better for ad hoc network with comparative study of existing intrusion detections
in ad hoc network. This research proposed a new approach called Multiple Intrusion detection systems where
the anomaly dataset is measured by the Neighborhood Outlier Factor (NOF). Here, trained model consists of
big datasets with distributed storage environment for improving the performance of the proposed Intrusion
Detection system. The experimental results proved that the proposed system identifies the anomalies very
effectively than any other approaches. The experimental results proved that the proposed system identifies the
anomalies very effectively than any other approaches.
ER  -