  Sensors (e.g., light, gyroscope, accelerotmeter) and sensing enabled
applications on a smart device make the applications more user-friendly and
efficient. However, the current permission-based sensor management systems of
smart devices only focus on certain sensors and any App can get access to other
sensors by just accessing the generic sensor API. In this way, attackers can
exploit these sensors in numerous ways: they can extract or leak users'
sensitive information, transfer malware, or record or steal sensitive
information from other nearby devices. In this paper, we propose 6thSense, a
context-aware intrusion detection system which enhances the security of smart
devices by observing changes in sensor data for different tasks of users and
creating a contextual model to distinguish benign and malicious behavior of
sensors. 6thSense utilizes three different Machine Learning-based detection
mechanisms (i.e., Markov Chain, Naive Bayes, and LMT) to detect malicious
behavior associated with sensors. We implemented 6thSense on a sensor-rich
Android smart device (i.e., smartphone) and collected data from typical daily
activities of 50 real users. Furthermore, we evaluated the performance of
6thSense against three sensor-based threats: (1) a malicious App that can be
triggered via a sensor (e.g., light), (2) a malicious App that can leak
information via a sensor, and (3) a malicious App that can steal data using
sensors. Our extensive evaluations show that the 6thSense framework is an
effective and practical approach to defeat growing sensor-based threats with an
accuracy above 96% without compromising the normal functionality of the device.
Moreover, our framework costs minimal overhead.
