Intrusion Detection System using Outlier Analysis
Umang Gupta, Shikha Gupta, Vandana Bhattacharjee · Zenodo (CERN European Organization for Nuclear Research) · 2019
The tremendous advancement and increase in usage of internet have increased the number of intruders and hackers over the years. These cyber-attacks increase in complexity and sophistication day by day, and it is thus proposed that data mining be used to counter them. Data mining is one of the recent technologies suitably applied to intrusion detection to detect the network attacks, to reduce the complexities and to get normal behavioral pattern. In this paper, an outlier analysis model is introduced which can be effectively used for anomaly detection. This paper proposes a neighborhood-based technique for outlier detection, where the Level of Outlierness (LON) of network dataset is measured to detect the outliers, if any.