LocAuth: A fine-grained indoor location-based authentication system using wireless networks characteristics

Abstract

Location-based information has become an attractive attribute for use in many services including localization, tracking, positioning, and authentication. An additional layer of security can be obtained by verifying the identity of users who wish to access confidential resources only within restricted, small, indoor trusted zones. The objective of this paper is to construct highly secure indoor areas primarily by detecting only legitimate users within their work cubicles. In this paper, we present a fine-grained location-based authentication system (LocAuth) which ensures the physical presence of the user within his/her small trusted zone (2 m2 area). To do this, LocAuth exploits the ambient wireless network characteristics (e.g., BSSID, SSID, and RSSI) of nearby Wi-Fi and Bluetooth devices observed from each trusted zone. We propose a novel technique called Top-Ranked Network Nodes (TRNNs) to accurately overcome the fluctuations in wireless signals and enhance the ability to distinguish targeted trusted zone from neighboring areas. In addition, we developed an application to implement LocAuth on Android-based smartphones and tested it in a real indoor environment. The tested area is composed of seven adjacent and closely spaced work cubicles located in our lab. We evaluated LocAuth in two ways: through RSSI-based nearest neighbors (RSSI-based NN) and through supervised machine learning algorithm (Support Vector Machines). The results of the experiment show the effectiveness of LocAuth by achieving a high classification accuracy (above 98%). This demonstrates its feasibility in terms of both accuracy as well as fine-grained classification.