Abstract-With the exponential growth of cyber-physical systems (CPS), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lack a systematic study of CPS security issues. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it very difficult to study the problem with one generalized model.In this paper, we capture and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: (1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; (2)from the CPS components perspective, we focus on cyber, physical, and cyber-physical components; and (3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS and smart cars). The model can be both abstract to show general interactions of a CPS application and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection.

With the increased popularity of ubiquitous computing and connectivity, the Internet of Things (IoT) also introduces new vulnerabilities and attack vectors. While secure data collection (i.e. the upward link) has been well studied in the literature, secure data dissemination (i.e. the downward link) remains an open problem. Attribute-based encryption (ABE) and outsourced-ABE has been used for secure message distribution in IoT, however, existing mechanisms suffer from extensive computation and/or privacy issues. In this paper, we explore the problem of privacy-preserving targeted broadcast in IoT. We propose two multi-cloud-based outsourced-ABE schemes, namely the parallel-cloud ABE and the chain-cloud ABE, which enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing user attributes from being disclosed. In particular, the proposed solution protects three types of privacy (i.e., data, attribute and access policy privacy) by enforcing collaborations among multiple clouds. Our schemes also provide delegation verifiability that allows the receivers to verify whether the clouds have faithfully performed the outsourced operations. We extensively analyze the security guarantees of the proposed mechanisms and demonstrate the effectiveness and efficiency of our schemes with simulated resource-constrained IoT devices, which outsource operations to Amazon EC2 and Microsoft Azure.

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