Evaluating the impact of contextual information on the performance of intelligent continuous authentication systems

Nowadays, the usage of computers ranges from activities that do not consider sensitive data, such as playing video games, to others managing confidential information, like military operations. Additionally, regardless of the actions performed by subjects, most computers store different pieces of sensitive data, making the implementation of robust security mechanisms a critical and mandatory task. In this context, continuous authentication has been proposed as a complementary mechanism to improve the limitations of conventional authentication methods. However, mainly driven by the evolution of Machine Learning (ML), a series of challenges related to authentication performance and, therefore, the feasibility of existing systems are still open. This work proposes the usage of contextual information related to the applications executed in the computers to create ML models able to authenticate subjects continuously. To evaluate the suitability of the proposed context-aware ML models, a continuous authentication framework for computers has been designed and implemented. Then, a set of experiments with a public dataset with 12 subjects demonstrated the improvement of the proposed approach compared to the existing ones. Precision, recall, and F1-Score metrics are raised from an average of 0.96 (provided by general ML models proposed in the literature) to 0.99-1.