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An Adaptive Multimodal Biometric Framework for Intrusion Detection in Online Social Networks


Ja'far Alqatawna


Vol. 15  No. 4  pp. 19-25


In this paper we propose an adaptive intrusion detection framework which can be applied to various Online Social Networking Platforms (OSNPs). The idea is based on the fact that most OSNPs are extremely interactive which implies that the activities of the users in these platforms can generate substantial information which can be used to build behavioral models to continuously authenticate users and detect any intrusion attempt. The framework is adaptive in the sense it can use several sources to obtain authentication information based on the type of the device used to access a particulars OSN. These sources include keystroke dynamics, mouse dynamics, touch dynamics, and other behavioral activities.


Intrusion Detection, Continuous Authentication, Biometrics, Multimodal, Keystroke Dynamics, Mouse Dynamics, Security Mechanism, Online Social Networks