Systematically Ensuring the Confidence of Real-Time Home Automation IoT Systems

Abstract

Recent advances and industry standards in Internet of Things (IoT) have accelerated the real-world adoption of connected devices. To manage this hybrid system of digital real-time devices and analog environments, the industry has pushed several popular home automation IoT (HA-IoT) frameworks, such as If-This-Then-That (IFTTT), Apple HomeKit, and Google Brillo. Typically, users author device interactions by specifying the triggering sensor event and the triggered device command. In this seemingly simple software system, two dominant factors govern the system confidence properties with respect to the physical world. First, IoT users are largely nonexperts who lack the comprehensive consideration regarding potential impact and joint effect with existing rules. Second, while the increasing complexity of IoT devices enables fine-grained control (e.g., heater temperature) of continuous real-time environments, even two simply connected devices can have a huge state space to explore. In fact, bugs that wrongfully control devices and home appliances can have ramifications on system correctness and even user physical safety. It is crucial to help users to make sure the system they created meets their expectation. In this article we introduce how techniques from hybrid automata can be practically applied to assist nonexpert IoT users in the confidence checking of such hybrid HA-IoT systems. We propose an automated framework for end-to-end programming assistance. We build and check the Linear Hybrid Automata (LHA) model of the system automatically. We also present a quantifier elimination-based method to analyze the counterexample found and synthesize fix suggestions. We implemented a platform, MenShen, based on this framework and proposed techniques. We conducted sets of real HA-IoT case studies with up to 46 devices and 65 rules. Empirical results show that MenShen can find violations and generate rule fix suggestions in only 10 seconds.

IoTSafe: Enforcing Safety and Security Policy with Real IoT Physical Interaction Discovery

“…[11] Peeves [7] Helion [28] iRuler [43] IoTMon [18] Menshen [8] HomeGuard [15] iRuler [43] uses SMT and model checking to detect vulnerabilities among IFTTT rules, especially in rule configurations of IoT deployments. Menshen [8] builds and checks the Linear Hybrid Automata (LHA) model for user rules to find violations and generate fix suggestions for non-expert users. Home-Guard [15] uses SMT models to check rules and configurations to find cross-app interference threats among smart home apps.…”

Charting the Attack Surface of Trigger-Action IoT Platforms

“…For example, it deines the attributes and commands of a device type, the efects on environmental variables (e.g., increasing temperature) of a command, and state transitions of a device command (i.e., what events will be generated by the execution of a command). Device/Service metadata can be constructed by analyzing the documentation of an IoT platform or provided by the platform developers or experts [28,51]. For the IFTTT platform, we construct the service metadata by crawling the web page of each service to get what triggers and actions the service supports.…”

“…7 Conversely, these works also provide several useful properties that we did not consider in iRuler. AutoTap [90] presents a method for verifying coniguration properties as expressed by novice users, and joins MenShen [28], Salus [51], and SIFT [52] in supporting automated creation and repair of rules (Rule Writing). Systems like Soteria [30], IoTSan [70], and Home-Guard (arXiv preprint only: [32]) are based on source code analysis of IoT apps and can therefore consider additional factors such as iner-grained reduction of state explosion and speciic malicious input sequences.…”

Towards a Natural Perspective of Smart Homes for Practical Security and Safety Analyses

“…For instance, prior systems designed to evaluate or enforce the safety/security of home automation have one trait in common: their reliance on safety/security properties that are manually specified, either by users (e.g., in case of Menshen [37] and AutoTAP [38]) or domain experts (e.g., IoTSAN [10], Soteria [4], IoTGuard [9], or ProvThings [7]). The practical policy specification approach enabled by H lion's scenarios (Sec.…”