Identifying untrusted interactive behaviour in Enterprise Resource Planning systems based on a big data pattern recognition method using behavioural analytics
Qian Yi, Mengyao Xu, Shuping Yi, Shiquan Xiong · Behaviour and Information Technology · 2020
To improve the performance of enterprise network information security, we proposed a behaviour analytics model that established a unique behaviour pattern for each user and identifies untrusted interactive behaviour. First, a series of behaviour characteristics was constructed by observing user behaviours. These characteristics were then used by a big data analysis method called hidden Markov model to model the behaviour of trusted users. Next, a forward algorithm calculated the probability of observation sequences from users with the same and different positions. Finally, untrusted interactive behaviours were identified by comparing the observation sequence probability sets of trusted and untrusted users. The proposed method was applied to the Enterprise Resource Planning system used by a publishing house to identify the credibility of its user behaviour. The highest false positive rates obtained were 0.74% and 5.26% for users in different positions and the same position, respectively. These results verify that the model is effective in identifying untrusted interactive behaviours.