No abstract

Traditional execution environments deploy Address Space Layout Randomization (ASLR) to defend against memory corruption attacks. However, Intel Software Guard Extension (SGX), a new trusted execution environment designed to serve security-critical applications on the cloud, lacks such an effective, well-studied feature. In fact, we find that applying ASLR to SGX programs raises non-trivial issues beyond simple engineering for a number of reasons: 1) SGX is designed to defeat a stronger adversary than the traditional model, which requires the address space layout to be hidden from the kernel; 2) the limited memory uses in SGX programs present a new challenge in providing a sufficient degree of entropy; 3) remote attestation conflicts with the dynamic relocation required for ASLR; and 4) the SGX specification relies on known and fixed addresses for key data structures that cannot be randomized. This paper presents SGX-Shield, a new ASLR scheme designed for SGX environments. SGX-Shield is built on a secure in-enclave loader to secretly bootstrap the memory space layout with a finer-grained randomization. To be compatible with SGX hardware (e.g., remote attestation, fixed addresses), SGX-Shield is designed with a software-based data execution protection mechanism through an LLVM-based compiler. We implement SGX-Shield and thoroughly evaluate it on real SGX hardware. It shows a high degree of randomness in memory layouts and stops memory corruption attacks with a high probability. SGX-Shield shows 7.61% performance overhead in running common microbenchmarks and 2.25% overhead in running a more realistic workload of an HTTPS server. § This work is done while these authors were visiting and Ph.D. students in Georgia Institute of Technology. system and hypervisor. It also offers hardware-based measurement, attestation, and enclave page access control to verify the integrity of its application code. Unfortunately, we observe that two properties, namely, confidentiality and integrity, do not guarantee the actual security of SGX programs, especially when traditional memory corruption vulnerabilities, such as buffer overflow, exist inside SGX programs. Worse yet, many existing SGX-based systems tend to have a large code base: an entire operating system as library in Haven [12] and a default runtime library in SDKs for Intel SGX [28, 29]. Further, they are mostly written in unsafe programming languages (e.g., C and C++) or often in an assembly language to provide direct compatibility with the Intel SGX hardware and to support its instruction sets. Running such a large code base inside an enclave altogether simply makes SGX programs vulnerable to traditional memory corruption attacks, facing the same security challenges as typical computer environments. This not only nullifies the security guarantee that SGX claims to provide, but also, perhaps more critically, allows attackers to exploit isolation and confidentiality to lurk-there is no way to know what the compromised enclave runs, and even worse, no way to analyze or monito...

Inter-datacenter wide area networks (inter-DC WAN) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization.This paper addresses the problem by introducing a Deadline-based Network Abstraction (DNA) for inter-DC WANs. DNA allows users to explicitly specify the amount of data to be delivered and the deadline by which it has to be completed. The malleability of DNA provides flexibility in resource allocation. Based on this, we develop a system called Amoeba that implements DNA. Our simulations and testbed experiments show that Amoeba, by harnessing DNA's malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.

Abstract. Many previous studies have examined the placement of access points (APs) to improve the community's understanding of the deployment and behavioral characteristics of wireless networks. A key implicit assumption in these studies is that one can estimate the AP location accurately from wardriving-like measurements. However, existing localization algorithms exhibit high error because they over-simplify the complex nature of signal propagation. In this work, we propose a novel approach that localizes APs using directional information derived from local signal strength variations. Our algorithm only uses signal strength information, and improves localization accuracy over existing techniques. Furthermore, the algorithm is robust to the sampling biases and nonuniform shadowing, which are common in wardriving measurements.