| Title | LOG-OFF: A Novel Behavior Based Authentication Compromise Detection Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Liu, Mingchang, Sachidananda, Vinay, Peng, Hongyi, Patil, Rajendra, Muneeswaran, Sivaanandh, Gurusamy, Mohan |
| Conference Name | 2022 19th Annual International Conference on Privacy, Security & Trust (PST) |
| Date Published | aug |
| Keywords | authentication, Bayes methods, Behavioral sciences, Collaboration, false trust, policy-based governance, Probabilistic logic, pubcrawl, resilience, Resiliency, Scalability, security, Training, user experience |
| Abstract | Password-based authentication system has been praised for its user-friendly, cost-effective, and easily deployable features. It is arguably the most commonly used security mechanism for various resources, services, and applications. On the other hand, it has well-known security flaws, including vulnerability to guessing attacks. Present state-of-the-art approaches have high overheads, as well as difficulties and unreliability during training, resulting in a poor user experience and a high false positive rate. As a result, a lightweight authentication compromise detection model that can make accurate detection with a low false positive rate is required.In this paper we propose - LOG-OFF - a behavior-based authentication compromise detection model. LOG-OFF is a lightweight model that can be deployed efficiently in practice because it does not include a labeled dataset. Based on the assumption that the behavioral pattern of a specific user does not suddenly change, we study the real-world authentication traffic data. The dataset contains more than 4 million records. We use two features to model the user behaviors, i.e., consecutive failures and login time, and develop a novel approach. LOG-OFF learns from the historical user behaviors to construct user profiles and makes probabilistic predictions of future login attempts for authentication compromise detection. LOG-OFF has a low false positive rate and latency, making it suitable for real-world deployment. In addition, it can also evolve with time and make more accurate detection as more data is being collected. |
| DOI | 10.1109/PST55820.2022.9851969 |
| Citation Key | liu_log-off_2022 |
LOG-OFF: A Novel Behavior Based Authentication Compromise Detection Approach