Sensors (e.g., light, gyroscope, and 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 percent without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.

中文翻译：

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用于检测智能设备上基于传感器的威胁的上下文感知框架

智能设备上的传感器（例如，光、陀螺仪和加速度计）和启用传感的应用程序使应用程序更加用户友好和高效。然而，目前智能设备的基于权限的传感器管理系统只关注某些传感器，任何应用程序只需访问通用传感器应用程序编程接口（API）即可访问其他传感器。通过这种方式，攻击者可以通过多种方式利用这些传感器：他们可以提取或泄露用户的敏感信息、传输恶意软件，或者记录或窃取附近其他设备的敏感信息。在本文中，我们提出了 6thSense，上下文感知入侵检测系统，通过观察用户不同任务的传感器数据变化，并创建上下文模型来区分传感器的良性和恶意行为，从而增强智能设备的安全性。6thSense 使用三种不同的基于机器学习的检测机制（即马尔可夫链、朴素贝叶斯和 LMT）。我们在多个传感器丰富的基于 Android 的智能设备（即智能手表和智能手机）上实施了 6thSense，并从 100 名真实用户的典型日常活动中收集了数据。此外，我们评估了 6thSense 针对三种基于传感器的威胁的性能：（1）可以通过传感器触发的恶意应用程序，（2）可以通过传感器泄漏信息的恶意应用程序，以及（3）恶意应用程序可以使用传感器窃取数据。我们的广泛评估表明，6thSense 框架是一种有效且实用的方法，可以在不影响设备正常功能的情况下以超过 96% 的准确度击败不断增长的基于传感器的威胁。此外，我们的框架揭示了最小的开销。