LOG-OFF: A Novel Behavior Based Authentication Compromise Detection Approach

Liu, Mingchang; Sachidananda, Vinay; Peng, Hongyi; Patil, Rajendra; Muneeswaran, Sivaanandh; Gurusamy, Mohan

doi:10.1109/pst55820.2022.9851969

Cited by 1 publication

(1 citation statement)

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“…In [8], a risk engine evaluates the contextual information, e.g., IP address and user agent, surrounding the login attempt to determine if further information is needed to safely assess the online user's identity. More recently, the author of [10] proposed a risk engine based on Bayesian inference that assess the level of confidence of a login attempt using the historic number of failed login attempts. In [12], the authors proposed a risk-based authentication system for mobile passenger at land/sea border controls based on novelty detection machine learning.…”

Section: Related Work a Risk-based Authentication Systemsmentioning

confidence: 99%

RLAuth: A Risk-Based Authentication System Using Reinforcement Learning

Picard¹,

Pierre²

2023

IEEE Access

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Conventional authentication systems, that are used to protect most modern mobile applications, are faced with usability and security problems related to their static and one-shot nature. Indeed, one-shot authentication mechanisms challenge the user at the beginning of a session leaving them vulnerable to attacks on lost/stolen devices or session hijacking. In addition, static authentication mechanisms always use the same challenges to authenticate the user without considering the dynamic nature of the risk related to the authentication context. To mitigate these challenges, we propose RLAuth, a risk-based authentication system that can automatically adapt the level of challenge presented to the user on each authentication request based on the current context. RLAuth is based on binary anomaly detection, which is solved using a deep reinforcement learning agent that acts as the classifier. To cope with the high class imbalance in the anomaly detection problem, we propose to use a balanced sampling technique during experience replay and an imbalanced correction factor during reward computation. We evaluate RLAuth on a public dataset using the G-mean metric which is the square root of the product of sensitivity with specificity. This metric is efficient to measure the classification performance of a model under class imbalance since it does not overfit to the majority class. Finally, RLAuth obtained a G-Mean of 92.62%. In addition, the reinforcement learning agent can be trained offline for acceptable results in about 130 s and can then be periodically retrained to improve its performance over time.

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Section: Related Work a Risk-based Authentication Systemsmentioning

confidence: 99%