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Internet of Things (IoT) frameworks are designed to facilitate provisioning and secure operation of IoT devices. A typical IoT framework consists of various software layers and components including third-party libraries, communication protocol stacks, the Hardware Abstraction Layer (HAL), the kernel, and the apps. IoT frameworks have implicit data flows in addition to explicit data flows due to their event-driven nature. In this paper, we present a static taint tracking framework, IFLOW, that facilitates the security analysis of system code by enabling specification of data-flow queries that can refer to a variety of software entities. We have formulated various security relevant data-flow queries and solved them using IFLOW to analyze the security of several popular IoT frameworks: Amazon FreeRTOS SDK, SmartThings SDK, and Google IoT SDK. Our results show that IFLOW can both detect real bugs and localize security analysis to the relevant components of IoT frameworks.
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Inversion and spectral analysis of matrices arising in the analysis of Markov processes
- Award ID(s):
- 1662629
- PAR ID:
- 10669477
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Annals of Operations Research
- Volume:
- 354
- Issue:
- 3
- ISSN:
- 0254-5330
- Page Range / eLocation ID:
- 1145 to 1170
- Subject(s) / Keyword(s):
- AMS Subject Classification: 15A29· 15A18· 15B51· 60G20 Keywords: Matrix inverse, Queueing, Stochastic process, Eigenvalues, Stationary distribution
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1007/s10479-019-03460-3
