As cloud computing gains widespread adoption across various industries, securing cloud resources has become a top priority for cloud providers. However, ensuring configuration security among highly interconnected cloud resources is challenging due to the complexities of resource modeling, correlation analysis, and large-scale security checks. To tackle those practical challenges, we propose Security Invariants (SI), a precise, effective, and scalable tool that proactively protects cloud resources by automated security reasoning. We have integrated SI into the rigorous Amazon Web Services (AWS) security review process. Partnered with security engineers and other security scanners, SI periodically scans billions of cloud resources in pre-launch services for potential security risks, maximizing the security guarantees of cloud applications. The continuous assessment of evolving resources not only brings a deep understanding of cloud security risks but also introduces a generalized solution from the holistic security analysis perspective.

Abstract Meaning Representation is a sentence-level meaning representation, which abstracts the meaning of sentences into a rooted acyclic directed graph. With the continuous expansion of Chinese AMR corpus, more and more scholars have developed parsing systems to automatically parse sentences into Chinese AMR. However, the current parsers can’t deal with concept alignment and relation alignment, let alone the evaluation methods for AMR parsing. Therefore, to make up for the vacancy of Chinese AMR parsing evaluation methods, based on AMR evaluation metric smatch, we have improved the algorithm of generating triples so that to make it compatible with concept alignment and relation alignment. Finally, we obtain a new integrity metric align-smatch for paring evaluation. A comparative research then was conducted on 20 manually annotated AMR and gold AMR, with the result that align-smatch works well in alignments and more robust in evaluating arcs. We also put forward some fine-grained metric for evaluating concept alignment, relation alignment and implicit concepts, in order to further measure parsers’ performance in subtasks.

There is an increasing body of work in the area of hardware defenses for software-driven security attacks. A significant challenge in developing these defenses is that the space of security vulnerabilities and exploits is large and not fully understood. This results in specific point defenses that aim to patch particular vulnerabilities. While these defenses are valuable, they are often blindsided by fresh attacks that exploit new vulnerabilities. This article aims to address this issue by suggesting ways to make future defenses more durable based on an organization of security vulnerabilities as they arise throughout the program life cycle. We classify these vulnerability sources through programming, compilation, and hardware realization, and we show how each source introduces unintended states and transitions into the implementation. Further, we show how security exploits gain control by moving the implementation to an unintended state using knowledge of these sources and how defenses work to prevent these transitions. This framework of analyzing vulnerability sources, exploits, and defenses provides insights into developing durable defenses that could defend against broader categories of exploits. We present illustrative case studies of four important attack genealogies—showing how they fit into the presented framework and how the sophistication of the exploits and defenses have evolved over time, providing us insights for the future.

Attacks often succeed by abusing the gap between program and machine-level semantics-- for example, by locating a sensitive pointer, exploiting a bug to overwrite this sensitive data, and hijacking the victim program's execution. In this work, we take secure system design on the offensive by continuously obfuscating information that attackers need but normal programs do not use, such as representation of code and pointers or the exact location of code and data. Our secure hardware architecture, Morpheus, combines two powerful protections: ensembles of moving target defenses and churn. Ensembles of moving target defenses randomize key program values (e.g., relocating pointers and encrypting code and pointers) which forces attackers to extensively probe the system prior to an attack. To ensure attack probes fail, the architecture incorporates churn to transparently re-randomize program values underneath the running system. With frequent churn, systems quickly become impractically difficult to penetrate. We demonstrate Morpheus through a RISC-V-based prototype designed to stop control-flow attacks. Each moving target defense in Morpheus uses hardware support to individually offer more randomness at a lower cost than previous techniques. When ensembled with churn, Morpheus defenses offer strong protection against control-flow attacks, with our security testing and performance studies revealing: i) high-coverage protection for a broad array of control-flow attacks, including protections for advanced attacks and an attack disclosed after the design of Morpheus, and ii) negligible performance impacts (1%) with churn periods up to 50 ms, which our study estimates to be at least 5000x faster than the time necessary to possibly penetrate Morpheus.

Malicious software, referred to as malware, continues to grow in sophistication. Past proposals for malware detection have primarily focused on software-based detectors which are vulnerable to being compromised. Thus, recent work has proposed hardware-assisted malware detection. In this paper, we introduce a new framework for hardware-assisted malware detection based on monitoring and classifying memory access patterns using machine learning. This provides for increased automation and coverage through reducing user input on specific malware signatures. The key insight underlying our work is that malware must change control flow and/or data structures, which leaves fingerprints on program memory accesses. Building on this, we propose an online framework for detecting malware that uses machine learning to classify malicious behavior based on virtual memory access patterns. Novel aspects of the framework include techniques for collecting and summarizing per-function/system-call memory access patterns, and a two-level classification architecture. Our experimental evaluation focuses on two important classes of malware (i) kernel rootkits and (ii) memory corruption attacks on user programs. The framework has a detection rate of 99.0% with less than 5% false positives and outperforms previous proposals for hardware-assisted malware detection.