Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System
This addresses mobile security for users by offering a more usable and secure alternative to traditional passwords, though it appears incremental as it applies an existing method (ANFIS) to a specific domain.
The paper tackled mobile security by proposing an adaptive neuro-fuzzy inference system (ANFIS) for continuous implicit authentication, achieving a 95% user recognition rate in experiments with behavioral data collected over 12 weeks.
As mobile devices have become indispensable in modern life, mobile security is becoming much more important. Traditional password or PIN-like point-of-entry security measures score low on usability and are vulnerable to brute force and other types of attacks. In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner.To illustrate the applicability and capability of ANFIS in our implicit authentication system, experiments were conducted on behavioural data collected for up to 12 weeks from different Android users. The ability of the ANFIS-based system to detect an adversary is also tested with scenarios involving an attacker with varying levels of knowledge. The results demonstrate that ANFIS is a feasible and efficient approach for implicit authentication with an average of 95% user recognition rate. Moreover, the use of ANFIS-based system for implicit authentication significantly reduces manual tuning and configuration tasks due to its selflearning capability.