A Study on the Detection of Abnormal Behavior and Vulnerability Analysis in BYOD

Abstract

When many companies recently introduced BYOD (Bring Your Own Device), i.e. allowing employees to use personal mobile devices at work, they also adopted the NAC and MDM system for prevention of confidential information leakage, access control and efficient user management. As the access control policy of the NAC and MDM system is uniformly applied to users, however, they cannot be aggressive in implementing BYOD since there are security threats due to the frequent loss and theft of devices and low security. Accordingly, it is necessary to be able to flexibly set up policies and detect and control abnormal users by collecting personalized context information. This paper proposes a behavior-based abnormality detection method that detects abnormal behavior by classifying vulnerabilities occurring in the BYOD environment and patterning various users’ information use contexts.