Iota: A Framework for Analyzing System-Level Security of IoTs
This addresses security vulnerabilities in IoT systems for users and administrators, offering a novel approach to comprehensive analysis.
The paper tackles the problem of limited attack detection in IoT security by proposing Iota, a framework for system-level security analysis that generates attack graphs, achieving over 80% accuracy in vulnerability prediction and revealing 62.8% of attack traces not anticipated by administrators.
Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. 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. In this work, we propose Iota, a logic programming-based framework to perform system-level security analysis for IoT systems. Iota generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building Iota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. 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 addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. 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. We apply Iota to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.