Implicit Authentication through Learning User Behavior

By Elaine Shi, Y. Niu, Markus Jakobsson, Richard Chow. Information Security Conference, 2010

313 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/820753b2151d5fd4892c337b134978a159fc5388

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In Information Systems

In Sociology and Political Science

Dusist Niyato

Citations in the corpus

Random forest for credit card fraud detection (2018, background): “[9] propose a user behavior model which treats the transaction features independently.”
A survey of mobile cloud computing: architecture, applications, and approaches (2013, methodology): “This scheme combines TrustCube [94] and implicit authentication [95,96] to authenticate the mobile clients.”
Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, unknown): “Note that the terms continuous authentication, active authentication [8], implicit authentication [9], [10], and transparent authentication [11] have been used interchangeably in the literature.”
Secure integration of IoT and Cloud Computing (2018, background): “Therefore, to gain consumers trust in the mobile cloud, the application models must support application development with privacy protection, and implicit authentication mechanisms [39] [43].”
HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users (2015, unknown): “Continuous or active authentication addresses these challenges by frequently and unobtrusively authenticating the user via behavioral biometric signals, such as touchscreen interactions [6], hand movements and gait [7], [8], voice [9], and phone location [10].”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “ng, therefore, has become the base platform for IoT devices to transport a user’s information and store it. Moreover, this could service makes IoT systems dynamic and updates it in a real-time manner [72]-[74]. Therefore, users who are utilizing similar clouds can have their data hacked, stolen, and”
Touch me once and i know it's you!: implicit authentication based on touch screen patterns (2012, background): “Shi et al. [17,31] also proposed the use of behavioral biometrics as replacement for password-based authentication or as second level of authentication.”
Multi-Factor Authentication: A Survey (2018, background): “…to monitor the driver-speciﬁc features [129,130], which could be analyzed from two perspectives: (i) vehicle-speciﬁc behavior: steering angle sensor, speed sensor, brake pressure sensor, etc. [131,132]; and (ii) human factors: music played, calls made, presence of people in the car, etc. [133].”
A Survey of Mobile Cloud Computing Application Models (2014, background): “Therefore, to gain consumers trust in the mobile cloud, the application models must support application development with privacy protection and implicit authentication mechanisms [58], [59].”