Outlier detection in data mining is an important arena where detection
models are developed to discover the objects that do not confirm the
expected behavior. The generation of huge data in real time applications
makes the outlier detection process into more crucial and challenging.
Traditional detection techniques based on mean and covariance are not
suitable to handle large amount of data and the results are affected by
outliers. So it is essential to develop an efficient outlier detection
model to detect outliers in the large dataset. The objective of this
research work is to develop an efficient outlier detection model for
multivariate data employing the enhanced Hidden Semi-Markov Model
(HSMM). It is an extension of conventional Hidden Markov Model (HMM)
where the proposed model allows arbitrary time distribution in its
states to detect outliers. Experimental results demonstrate the better
performance of proposed model in terms of detection accuracy, detection
rate. Compared to conventional Hidden Markov Model based outlier
detection the detection accuracy of proposed model is obtained as
98.62% which is significantly better for large multivariate datasets.
A Comparison of Malware Detection Techniques Based on Hidden Markov Model
