Author 1: Bhawana Pillai
Author 2: Uday Pratap Singh
Most anomaly based NIDS employ supervised
algorithms, whose performances highly depend on attack-free
training data. Moreover, with changing network environment or
services, patterns of normal traffic will be changed. In this paper,
we developed intrusion detection system is to analyses the
authentication records and separate UNFEIGNED and
fraudulent authentication attempts for each user account in the
system. Intrusions are detected by determining outliers related to
the built patterns. We present the modification on the outlier
detection algorithm. It is important problems to increase the
detection rates and reduce false positive rates in Intrusion
Detection System. Although preventative techniques such as
access control and authentication attempt to prevent intruders,
these can fail, and as a second line of defense, intrusion detection
has been introduced. Rare events are events that occur very
infrequently, detection of rare events is a common problem in
many domains. Support Vector Machines (SVM) as a classical
pattern recognition tool have been widely used for intrusion
detection. However, conventional SVM methods do not concern
different characteristics of features in building an intrusion detection system. Also evaluate the performance of K-Means algorithm by the detection rate and the false positive rate. All
result evaluate with the new model of KDD dataset. Result generates in ROC Curves and compared both result of K-Means and SVM in Matlab.
Anomaly detection
Intrusion Detection
Expectation Maximization
MATLAB
UNSOUND authentication
UNFEIGNED
reduce false.