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Outlier Detection in Stream Data by Machine Learning and Feature Selection Methods
Authors: Hossein Moradi Koupaie , Suhaimi Ibrahim , Javad Hosseinkhani
Number of views: 587
In recent years, intrusion detection has emerged as an important technique for network security. Machine
learning techniques have been applied to the field of intrusion detection. They can learn normal and
anomalous patterns from training data and via Feature selection improving classification by searching for
the subset of features which best classifies the training data to detect attacks on computer system. The
quality of features directly affects the performance of classification. Many feature selection methods
introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or
robustness of classification. Outlier detection in stream data is an important and active research issue in
anomaly detection. Most of the existing outlier detection algorithms has less accurate because use some
clustering method. Some data are so essential and secretary. Therefore, it needs to mine carefully even if
spend cost. This paper presents a framework to detect outlier in stream data by machine learning method.
Moreover, it is considered if data was high dimensional. This method is more accurate from other preferred
models, because machine learning method is more accurate of other methods.
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