PALOT: Profiling and Authenticating Users Leveraging Internet of Things.

Area of Science:

• Computer Science
• Cybersecurity
• Information Systems

Background:

• Traditional authentication methods face limitations and challenges.
• Continuous authentication offers improved user validation but requires advanced solutions.
• Existing frameworks struggle with context-aware and non-intrusive security.

Purpose of the Study:

• To present PALOT, a novel framework for context-aware, continuous, and non-intrusive authentication and authorization services.
• To leverage the Internet of Things (IoT) for enhanced user identity validation.
• To address the unsolved challenges in current continuous authentication methodologies.

Main Methods:

• Proposed a formal information system model based on ontologies for knowledge representation.
• Introduced a 'confidence manager' module to recognize user behavioral patterns within the IoT ecosystem.
• Integrated the confidence manager into an extended IoTCAF framework architecture.

Main Results:

• Demonstrated the efficacy of the PALOT framework through exhaustive experiments.
• Validated the feasibility and scalability of the proposed continuous authentication solution.
• Showcased the ability to recognize user behavioral patterns for improved security.

Conclusions:

• The PALOT framework effectively provides context-aware, continuous, and non-intrusive authentication using IoT.
• The ontology-based model and confidence manager are key components for recognizing user behavior.
• The solution is proven to be efficacious, feasible, and scalable for modern security needs.