Adaptive Microarchitectural Optimizations to Improve Performance and Security of Multi-Core Architectures

With the current technological barriers, microarchitectural optimizations are increasingly important to ensure performance scalability of computing systems. The shift to multi-core architectures increases the demands on the memory system, and amplifies the role of microarchitectural optimizations in performance improvement. In a multi-core system, microarchitectural resources are usually shared, such as the cache, to maximize utilization but sharing can also lead to contention and lower performance. This can be mitigated through partitioning of shared caches.However, microarchitectural optimizations which were assumed to be fundamentally secure for a long time, can be used in side-channel attacks to exploit secrets, as cryptographic keys. Timing-based side-channels exploit predictable timing variations due to the interaction with microarchitectural optimizations during program execution. Going forward, there is a strong need to be able to leverage microarchitectural optimizations for performance without compromising security. This thesis contributes with three adaptive microarchitectural resource management optimizations to improve security and/or\ua0performance\ua0of multi-core architectures\ua0and a systematization-of-knowledge of timing-based side-channel attacks.\ua0We observe that to achieve high-performance cache partitioning in a multi-core system\ua0three requirements need to be met: i) fine-granularity of partitions, ii) locality-aware placement and iii) frequent changes. These requirements lead to\ua0high overheads for current centralized partitioning solutions, especially as the number of cores in the\ua0system increases. To address this problem, we present an adaptive and scalable cache partitioning solution (DELTA) using a distributed and asynchronous allocation algorithm. The\ua0allocations occur through core-to-core challenges, where applications with larger performance benefit will gain cache capacity. The\ua0solution is implementable in hardware, due to low computational complexity, and can scale to large core counts.According to our analysis, better performance can be achieved by coordination of multiple optimizations for different resources, e.g., off-chip bandwidth and cache, but is challenging due to the increased number of possible allocations which need to be evaluated.\ua0Based on these observations, we present a solution (CBP) for coordinated management of the optimizations: cache partitioning, bandwidth partitioning and prefetching.\ua0Efficient allocations, considering the inter-resource interactions and trade-offs, are achieved using local resource managers to limit the solution space.The continuously growing number of\ua0side-channel attacks leveraging\ua0microarchitectural optimizations prompts us to review attacks and defenses to understand the vulnerabilities of different microarchitectural optimizations. We identify the four root causes of timing-based side-channel attacks: determinism, sharing, access violation\ua0and information flow.\ua0Our key insight is that eliminating any of the exploited root causes, in any of the attack steps, is enough to provide protection.\ua0Based on our framework, we present a systematization of the attacks and defenses on a wide range of microarchitectural optimizations, which highlights their key similarities.\ua0Shared caches are an attractive attack surface for side-channel attacks, while defenses need to be efficient since the cache is crucial for performance.\ua0To address this issue, we present an adaptive and scalable cache partitioning solution (SCALE) for protection against cache side-channel attacks. The solution leverages randomness,\ua0and provides quantifiable and information theoretic security guarantees using differential privacy. The solution closes the performance gap to a state-of-the-art non-secure allocation policy for a mix of secure and non-secure applications

Exploiting Code Diversity to Enhance Code Virtualization Protection

Code virtualization built upon virtual machine (VM)technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed set of virtual instructions and bytecode interpreters across programs. This, however, exposes a security vulnerability where an experienced attacker can use knowledge extracted from other programs to quickly uncover the mapping between virtual instructions and native code for applications protected under the same scheme. In this paper, we propose a novel VM-based code obfuscation system to address this problem. The core idea of our approach is to obfuscate the mapping between the opcodes of bytecode instructions and their semantics. We achieve this by partitioning each protected code region into multiple segments where the mapping of opcodes and their semantics is randomized in different ways in different segments. In this way, each bytecode instruction will be translated into different native code in different sections of the obfuscated code. This significantly increases the diversity of the program behavior. As a result, the knowledge of bytecode to native code mappings obtained from other programs will be less useful when targeting a new program. We evaluate our approach on a set of real-world applications and compare it against two state-of-the-art VM-based code obfuscation approaches. Experimental results show that our approach is effective, which provides stronger protection with comparable runtime overhead and code size

Capabilities for cross-layer micro-service security

Shared infrastructure computing has become ubiquitous; from the smallest start-up deploying on a multi-tenant cloud to the largest corporations whose separate branches all deploy to a shared private cloud. In both cases, the security challenges are similar and are unique from the legacy model of deploying monolithic applications on dedicated hardware. In the case of a multi-tenant cloud deployment, attacks can stem from other tenants who are not part of the same security domain, be that a different security-level within a single organization, or distinct organizations on a public cloud. In addition to nearly ubiquitous adoption of shared infrastructure, the rise of so called “micro-services” poses a set of unique challenges and advantages to security. The micro-service moniker stems from the idea of a Service Oriented Architecture (SOA) with a focus on having a small code base for each component of an application. The SOA approach is complimented by the DevOps movement in which software development practices are being applied to operations. These development and deployment techniques are here to stay as they enable more thorough testing, reliable deployment, and calability that previous software architectures only supported with extensive rewriting. In this dissertation, we focus on providing security to this new paradigm of computing. These trends force us to face security challenges unique to cloud computing such as passive cache-based side-channel attacks. In addition to new challenges, this new paradigm also affords us better tools and services due to the well-defined behavior of micro-services. Here, we focus on mitigating security risks by leveraging the Principle of Least Privilege (PoLP) at every layer of the stack: the interface between the operating system and the hardware, the system call interface, and within individual applications. We implement the PoLP through layer specific capabilities by mapping the security challenges present in cloud computing to a Take-Grant relational model between subjects. We conceptually extend the notion of “subject” to include subjects at every layer of the cloud stack. Additionally, we explore adding more trust guarantees to subject relationship monitoring. Finally, we explore fine grained memory operations within a micro-service that can impact a micro-service’s relationships with other subjects in the system