Attribute based encryption is a promising technique that achieves flexible and fine-grained data access control over encrypted data, which is very suitable for a secure data sharing environment such as the currently popular cloud computing. However, traditional attribute based encryption fails to provide an efficient keyword based search on encrypted data, which somewhat weakens the power of this encryption technique, as search is usually the most important approach to quickly obtain data of interest from large-scale dataset. To address this problem, attribute based encryption with keyword search (ABKS) is designed to achieve fine-grained data access control and keyword based search, simultaneously, by an ingenious combination of attribute based encryption and searchable encryption. Recently, several ABKS schemes have been constructed in secure cloud storage system for data access control and keyword search. Nonetheless, each of these schemes has some defects such as impractical computation overhead and insufficient access policy expression. To overcome these limitations, in this paper, we design a Key-Policy Searchable Attribute-based Encryption Scheme (KPSABES) based on the full-blown key-policy attribute-based encryption proposed by Vipul Goyal et al. By novel design, our scheme not only inherits all advantages of that scheme but also achieves efficient and secure keyword search over encrypted data. We provide the detailed performance analyses and security proofs for our scheme. Extensive experiments demonstrated that our proposed scheme is superior in many aspects to the similar work.

False data injection (FDI) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches.