IoT-HASS: A Framework For Protecting Smart Home Environment

While many solutions have been proposed for smart home security, the problem that no single solution fully protects the smart home environment still exists. In this research we propose a security framework to protect the smart home environment. The proposed framework includes three engines that complement each other to protect the smart home IoT devices. The first engine is an IDS/IPS module that monitors all traffic in the home network and then detects, alerts users, and/or blocks packets using anomaly-based detection. The second engine works as a device management module that scans and verifies IoT devices in the home network, allowing the user to flag any suspect device. The third engine works as a privacy monitoring module that monitors and detects information transmitted in plaintext and alerts the user if such information is detected. We call the proposed system IoT-Home Advanced Security System or IoT-HASS for short. IoT-HASS was developed using Python 3 and can be implemented in two modes of operation. The in-line mode allows the IoT-HASS to be installed in-line with the traffic inside a Raspberry Pi or a Router. In the in-line mode IoT-HASS acts as an IPS that can detect and block threats as well as alert the user. The second mode is the passive mode where IoT-HASS in not installed in-line with the traffic and can act as an IDS that passively monitors the traffic, detecting threats and alerting the user, but not blocking the attack. IoT-HASS was evaluated via four testing scenarios. It demonstrated superior performance in all testing scenarios in detecting attacks such as DDoS attacks, Brute Force Attacks, and Cross Site Scripting (XSS) Attacks. In each of the four test scenarios, we also tested the device management functionality, which we found to successfully scan and display IoT devices for the homeowner. The extensive evaluating and testing of IoT-HASS showed that IoT-HASS can successfully run in a small device such as a Raspberry Pi, and thus, it will most likely run in an embedded device as an IoT device. Our future research will concentrate on strengthening the current features of IoT-HASS to include additional functionalities

Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya