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Healthcare system is rapidly transitioning from a traditional health-center and specialist-focused model to a more scattered, patient-centric model. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. Experimental results show that our framework is effective and highly efficient. We apply Iota to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that Iota's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects.

To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In building Iota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. Iota generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces.

In this work, we propose Iota, a logic programming-based framework to perform system-level security analysis for IoT systems. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment.

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