Biblio

This paper proposes an intelligent functional architecture for an advanced launch site system that is composed of five parts: the intelligent technical area, the intelligent launching region, the intelligent flight and landing area, the intelligent command and control system, and the intelligent analysis assessment system. The five parts consist of the infrastructure, facilities, equipment, hardware and software and thus include the whole mission processes of ground and launch systems from flight articles' entry to launch. The architectural framework is designed for the intelligent elements of the parts. The framework is also defined as the interrelationship and the interface of the elements, including the launch vehicle and flight payloads. Based on the Internet of Things (IoT), the framework is integrated on four levels: the physical layer, the perception layer, the network layer, and the application layer. The physical layer includes the physical objects and actuators of the launch site. The perception layer consists of the sensors and data processing system. The network layer supplies the access gateways and backbone network. The application layer serves application systems through the middleware platform. The core of the intelligent system is the controller of the automatic control system crossing the four layers. This study builds the models of the IoT, cloud platform, middleware, integrated access gateway, and automatic control system for actual ground and launch systems. A formal approach describes and defines the architecture, models and autonomous control flows in the paper. The defined models describe the physical objects, intelligent elements, interface relations, status transformation functions, etc. The test operation and launch processes are connected with the intelligent system model. This study has been applied to an individual mission project and achieved good results. The architecture and the models of this study regulate the relationship between the elements of the intelligent system. The study lays a foundation for the architectural construction, the simulation and the verification of the intelligent systems at the launch site.

Intel Software Guard Extension (SGX) offers software applications a shielded execution environment, dubbed enclave, to protect their confidentiality and integrity from malicious operating systems. As processors with this extended feature become commercially available, many new software applications are developed to enrich to the SGX-enabled ecosystem. One important primitive for these applications is a secure communication channel between the enclave and a remote trusted party. The SSL/TLS protocol, which is the de facto standard for protecting transport-layer network communications, has been broadly regarded a natural choice for such purposes. However, in this paper, we show that the marriage between SGX and SSL may not be smooth sailing. Particularly, we consider a category of side-channel attacks against SSL/TLS implementations in secure enclaves, which we call the control-flow inference attacks. In these attacks, the malicious operating system kernel may perform a powerful man-in-the-kernel attack to collect execution traces of the enclave programs at the page level, the cacheline level, or the branch level, while positioning itself in the middle of the two communicating parties. At the center of our work is a differential analysis framework, dubbed Stacco, to dynamically analyze the SSL/TLS implementations and detect vulnerabilities-discernible execution traces-that can be exploited as decryption oracles. Surprisingly, in spite of the prevailing constant-time programming paradigm adopted by many cryptographic libraries, we found exploitable vulnerabilities in the latest versions of all the SSL/TLS libraries we have examined. To validate the detected vulnerabilities, we developed a man-in-the-kernel adversary to demonstrate Bleichenbacher attacks against the latest OpenSSL library running in the SGX enclave (with the help of Graphene) and completely broke the PreMasterSecret encrypted by a 4096-bit RSA public key with only 57286 queries. We also conducted CBC padding oracle attacks against the latest GnuTLS running in Graphene-SGX and an open-source SGX implementation of mbedTLS (i.e., mbedTLS-SGX) that runs directly inside the enclave, and showed that it only needs 48388 and 25717 queries, respectively, to break one block of AES ciphertext. Empirical evaluation suggests these man-in-the-kernel attacks can be completed within 1 or 2 hours. Our results reveal the insufficient understanding of side-channel security in SGX settings, and our study will provoke discussions on the secure implementation and adoption of SSL/TLS in secure enclaves.

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.