A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices

TL;DR

This paper introduces 6thSense, a context-aware intrusion detection system for smart devices that uses machine learning to detect sensor-based threats with high accuracy, enhancing device security without impacting normal functionality.

Contribution

The paper presents a novel, machine learning-based framework that detects sensor-based threats on smart devices by modeling user context, improving security beyond traditional permission systems.

Findings

Achieves over 96% detection accuracy.

Effective against three types of sensor-based threats.

Minimal performance overhead on devices.

Abstract

Sensors (e.g., light, gyroscope, accelerometer) 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 Application Programming Interface (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). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 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, (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 reveals minimal overhead.