International Journal of Engineering and Technology

Nowadays companies need to manage user accesses across multiple channels such as mobile, social and cloud. Business is always about delivering value to customers, and Identity and Access Management is very essential part of ensuring that employees are both empowered to deliver that value and prohibited from damaging the security, business reputation. Organization can use Identity and Access data to detect the attacks within the organization. Data Analytics techniques can be used to identify the anomalies in the identity and Access Data, out of that Peer group Analysis is one technique. Peer Group Analysis focuses on the local pattern rather than the global models; anomaly may not be present when compared with whole data but there may be some outliers present when compared with its peer groups. Context anomaly is the data points that considered abnormal when compared with its peer group. In this research we are doing peer group analysis on Identity and Access Data using the various outlier detection algorithms. Various context anomaly detection algorithms are used to identify the abnormal user behaviour which gives the risk insights the organization. We are proposing peer group analysis which can able to locate entitlements that can be exclusive than other users of same group, which gives the risk of the user access.