A Comprehensive Security Framework for Securing Sensors in Smart Devices and Applications

This doctoral dissertation introduces novel security frameworks to detect sensor-based threats on smart devices and applications in smart settings such as smart home, smart office, etc. First, we present a formal taxonomy and in-depth impact analysis of existing sensor-based threats to smart devices and applications based on attack characteristics, targeted components, and capabilities. Then, we design a novel context-aware intrusion detection system, 6thSense, to detect sensor-based threats in standalone smart devices (e.g., smartphone, smart watch, etc.). 6thSense considers user activity-sensor co-dependence in standalone smart devices to learn the ongoing user activity contexts and builds a context-aware model to distinguish malicious sensor activities from benign user behavior. Further, we develop a platform-independent context-aware security framework, Aegis, to detect the behavior of malicious sensors and devices in a connected smart environment (e.g., smart home, offices, etc.). Aegis observes the changing patterns of the states of smart sensors and devices for user activities in a smart environment and builds a contextual model to detect malicious activities considering sensor-device-user interactions and multi-platform correlation. Then, to limit unauthorized and malicious sensor and device access, we present, kratos, a multi-user multi-device-aware access control system for smart environment and devices. kratos introduces a formal policy language to understand diverse user demands in smart environment and implements a novel policy negotiation algorithm to automatically detect and resolve conflicting user demands and limit unauthorized access. For each contribution, this dissertation presents novel security mechanisms and techniques that can be implemented independently or collectively to secure sensors in real-life smart devices, systems, and applications. Moreover, each contribution is supported by several user and usability studies we performed to understand the needs of the users in terms of sensor security and access control in smart devices and improve the user experience in these real-time systems

Peek-a-Boo: I see your smart home activities, even encrypted!

A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles through the sensors already embedded in these devices. Sensor data contains a lot of sensitive information about the user and devices. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information from the encrypted payload (i.e., sensor data) about the users and their activities, invading user privacy. With this in mind,in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying the types of IoT devices, their states, and ongoing user activities in a cascading style by only passively sniffing the network traffic from smart home devices and sensors. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed traffic to hide the device states and demonstrate that it provides better protection than existing solutions.Comment: Update (May 13, 2020): This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '20), July 8-10, 2020, Linz (Virtual Event), Austria, https://doi.org/10.1145/3395351.339942

Early diagnosis with alternative approaches: Innovation in lung cancer care

Cancer is one of the primary concerns of mortality throughout the world in the present day. Among different types of cancers, lung cancer (LC) is predominant (21.77% of overall cancer caused death). It is classified into two major categories: oat cell or small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The mortality due to NSCLC (~85%) is almost six times higher than the SCLC (15%). The identification of risk factors can reduce cancer burden and human fatality; however, this cannot prevent the severity of LC. In this case, the early diagnosis of LC can be an effective pathway in providing better treatment to the patients, which eventually may result in less fatality. This article offers an overview of several existing screening methods for the early diagnosis of LC. Nevertheless, these methodologies have limitations of reliability towards the diagnosis of cancer malignancy. Therefore, the prospects of different alternative approaches and their challenges to overcome this barrier for the early diagnosis of malignant tumor cells have been articulated in this article