DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization

Recent research has demonstrated that Intel's SGX is vulnerable to various software-based side-channel attacks. In particular, attacks that monitor CPU caches shared between the victim enclave and untrusted software enable accurate leakage of secret enclave data. Known defenses assume developer assistance, require hardware changes, impose high overhead, or prevent only some of the known attacks. In this paper we propose data location randomization as a novel defensive approach to address the threat of side-channel attacks. Our main goal is to break the link between the cache observations by the privileged adversary and the actual data accesses by the victim. We design and implement a compiler-based tool called DR.SGX that instruments enclave code such that data locations are permuted at the granularity of cache lines. We realize the permutation with the CPU's cryptographic hardware-acceleration units providing secure randomization. To prevent correlation of repeated memory accesses we continuously re-randomize all enclave data during execution. Our solution effectively protects many (but not all) enclaves from cache attacks and provides a complementary enclave hardening technique that is especially useful against unpredictable information leakage

Efficient Code Generation from SHIM Models

Programming concurrent systems is substantially more difficult than programming sequential systems, yet most embedded systems need concurrency. We believe this should be addressed through higher-level models of concurrency that eliminate many of the usual challenges, such as nondeterminism arising from races. The shim model of computation provides deterministic concurrency, and there already exist ways of implementing it in hardware and software. In this work, we describe how to produce more efficient C code from shim systems. We propose two techniques: a largely mechanical one that produces tail-recursive code for simulating concurrency, and a more clever one that statically analyzes the communication pattern of multiple processes to produce code with far less overhead. Experimentally, we find our tail-recursive technique produces code that runs roughly twice as fast as a baseline; our statically-scheduled code can run up to twelve times faster

Automatic transmit power control for power efficient communications in UAS

Nowadays, unmanned aerial vehicles (UAV) have become one of the most popular tools that can be used in commercial, scientific, agricultural and military applications. As drones become faster, smaller and cheaper, with the ability to add payloads, the usage of the drone can be versatile. In most of the cases, unmanned aerials systems (UAS) are equipped with a wireless communication system to establish a link with the ground control station to transfer the control commands, video stream, and payload data. However, with the limited onboard calculation resources in the UAS, and the growing size and volume of the payload data, computational complex signal processing such as deep learning cannot be easily done on the drone. Hence, in many drone applications, the UAS is just a tool for capturing and storing data, and then the data is post-processed off-line in a more powerful computing device. The other solution is to stream payload data to the ground control station (GCS) and let the powerful computer on the ground station to handle these data in real-time. With the development of communication techniques such as orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) transmissions, it is possible to increase the spectral efficiency over large bandwidths and consequently achieve high transmission rates. However, the drone and the communication system are usually being designed separately, which means that regardless of the situation of the drone, the communication system is working independently to provide the data link. Consequently, by taking into account the position of the drone, the communication system has some room to optimize the link budget efficiency. In this master thesis, a power-efficient wireless communication downlink for UAS has been designed. It is achieved by developing an automatic transmit power control system and a custom OFDM communication system. The work has been divided into three parts: research of the drone communication system, an optimized communication system design and finally, FPGA implementation. In the first part, an overview on commercial drone communication schemes is presented and discussed. The advantages and disadvantages shown are the source of inspiration for improvement. With these ideas, an optimized scheme is presented. In the second part, an automatic transmit power control system for UAV wireless communication and a power-efficient OFDM downlink scheme are proposed. The automatic transmit power control system can estimate the required power level by the relative position between the drone and the GCS and then inform the system to adjust the power amplifier (PA) gain and power supply settings. To obtain high power efficiency for different output power levels, a searching strategy has been applied to the PA testbed to find out the best voltage supply and gain configurations. Besides, the OFDM signal generation developed in Python can encode data bytes to the baseband signal for testing purpose. Digital predistortion (DPD) linearization has been included in the transmitter’s design to guarantee the signal linearity. In the third part, two core algorithms: IFFT and LUT-based DPD, have been implemented in the FPGA platform to meet the real-time and high-speed I/O requirements. By using the high-level synthesis design process provided by Xilinx Corp, the algorithms are implemented as reusable IP blocks. The conclusion of the project is given in the end, including the summary of the proposed drone communication system and envisioning possible future lines of research