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April 26, 2019, 02:00 PM
Spyridoula Gravani: Operating System Support for Lightweight Defense Against Code-Reuse Attacks

[Friday, April 26, 2019 at 2:00 PM in Wegmans Hall 2506]

Computer systems have become a vital part of modern society and are increasingly being used in safety- and security-critical tasks such as flight control, autonomous driving, home automation and electronic voting. The massive interconnectivity (so-called Internet of Things) along with our increasing reliance to technology make modern computing platforms both an appealing and an easy target: attackers obtain remote access to a system and exploit software vulnerabilities that trigger malicious activities. Commodity operating systems such as Windows, Linux, and MacOS form the trusted computing base of today’s computing systems, and execute with elevated privileges to protect applications and the underlying platform from malicious behavior. However, since modern operating systems are written in C and C++, they have memory safety errors and suffer from the same vulnerabilities as the applications they protect.

Control-flow hijacking attacks exploit memory corruption vulnerabilities to take over execution and control the behavior of a program. Whereas conventional control-flow attacks require the injection of the malicious payload, code-reuse attacks leverage code that already exists in the process address space, bypassing widely deployed data-execution prevention mechanisms. Advanced code-reuse attacks also exploit memory disclosure vulnerabilities to circumvent address space layout randomization protection schemes that randomize the process image on every execution. Existing defenses against kernel-level code-reuse attacks rely on static analysis to identify and label legitimate code paths, and add instrumentation to ensure that only tagged paths are followed during execution (control-flow integrity enforcement). Unfortunately, because static analysis is inevitably imprecise, such defenses are easily bypassed by code-reuse attacks, even under weak adversarial assumptions.

It is our thesis that a defense against code-reuse attacks in the operating system kernel cannot rely on static analysis. Following the strategy of defenses in user space, we instead propose a comprehensive solution that (1) diversifies code layout through fine-grained address space randomization, (2) protects against direct disclosure of the layout by making executable memory unreadable, and (3) prevents corruption of return addresses during execution. Our preliminary work presents IskiOS, a system that leverages Intel’s PKU hardware feature available in modern x86 processors to implement execute-only memory and protected shadow stacks in the kernel with very low run-time overhead.

For our future work, we propose a software-only technique, dubbed Mirroring Software-fault Isolation, that enables flexible isolation of multiple segments within a single address space and supports leakage-resilient diversification defenses against code-reuse attacks on systems lacking hardware support to enable execute-only memory permissions.

April 29, 2019, 09:30 AM
Linfeng Song: Tackling Graphical NLP problems with Graph Recurrent Networks

[Monday, April 29, 2019 at 9:30 AM in Wegmans Hall 2506]

How to properly model graphs is a long-existing and important problem in natural language processing, where several popular types of graphs are knowledge graphs, semantic graphs and dependency graphs. Comparing with other data structures, such as sequences and trees, graphs are generally more powerful in representing complex correlations among entities. For example, a knowledge graph stores real-word entities (such as “Barack Obama” and “U.S.”) and their relations (such as “live in” and “lead by”). Properly encoding a knowledge graph is beneficial to user applications, such as question answering and knowledge discovery. Modeling graphs is also very challenging, probably be-cause graphs usually contain massive and cyclic relations. For instance, a tree with n nodes has n − 1 edges (relations), while a complete graph with n nodes can have O(n2) edges (relations).

Recent years have witnessed the success of deep learning, especially RNN-based models, on many NLP problems, including machine translation (Cho et al., 2014) and question answering (Shen et al., 2017). Besides, RNNs and their variations have been extensively studied on several graph problems and showed preliminary successes. Despite the successes that have been achieved, RNN-based models suffer from several major drawbacks. First, they can only consume sequential data, thus linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, originally closely located graph nodes can be very far away after linearization, and this introduces great challenge for RNNs to model their relation. Second, the serialization results are usually very long, so it takes a long time for RNNs to encode them.

In this thesis, we propose a novel graph neural network, named graph re-current network (GRN). GRN takes a hidden state for each graph node, and it relies on an iterative message passing framework to update these hidden states in parallel. Within each iteration, neighboring nodes exchange information be-tween each other, so that they absorb more global knowledge. Different from RNNs, which require absolute orders (such as left-to-right orders) for execution, our GRN only require relative neighboring information, making it very general and flexible on a variety of data structures.

We study our GRN model on 4 very different tasks, such as machine read-ing comprehension, relation extraction and machine translation. Some tasks (such as machine translation) require generating sequences, while others only require one decision (classification). Some take undirected graphs without edge labels, while the others have directed ones with edge labels. To consider these important differences, we gradually enhance our GRN model, such as further considering edge labels and adding an RNN decoder. Carefully designed experiments show the effectiveness of GRN on all these tasks.

Reception to follow at 12:30pm in Wegmans Hall 2506

Advisor: Prof. Daniel Gildea (Computer Science)

Committee: Prof. Jiebo Luo (Computer Science), Prof. Lenhart Schubert (Computer Science), Prof. Yue Zhang (Westlake University)

April 29, 2019, 04:00 PM
Sharanyan Srikanthan: Sharing-Aware Resource Management for Multicore Systems

[Monday, April 29, 2019 at 4:00 PM in Goergen 108]

Efficient resource management is a growing problem due to the ever increasing scale and complexity of computational systems and the applications that use them. Modern multicore systems offer abundant compute resources to exploit application-level parallelism. The multiple compute cores within a single system share resources such as processor pipelines, caches, interconnects, and memory, presenting opportunities for efficient data sharing and resource utilization. These multicore systems are regularly employed in large clusters and clouds, hosting a multitude of applications and services simultaneously. While a tremendous amount of research has been aimed at solving various problems in offering infrastructure as a service, managing multiple applications and achieving high utilization and efficiency remains a challenge. The quest to increase utilization may result in higher resource contention and correspondingly unpredictable and often significantly degraded performance. Moreover, the degree of data and resources sharing among different cores is nonuniform, and is dependent on the architecture and applications involved. Identifying and enabling the efficient and synergistic sharing of data and resources while also minimizing resource contention and saturation is the key to simultaneously achieving higher utilization and resource efficiency.

In this dissertation, we argue that it is possible to provide efficient and deterministic performance in both individual and distributed multicore systems using a holistic approach that simultaneously guides application resource acquisition and manages hardware resources and task placement. Our resource management strategy combines information from the execution environment with application-defined quality of service targets to achieve overall system efficiency while meeting individual application progress guarantees. We demonstrate that aggregate information from low overhead hardware performance counters is sufficient to characterize individual application resource demands and bottlenecks specific to the execution environment. Using this information, we develop a hierarchical resource management strategy that can: monitor performance critical architectural resources and control task placement for optimal use of these resources; understand application bottlenecks, scalability, and parallel efficiency to reallocate resources while guaranteeing quality of service; and consolidate the above information from individual machines into a shared state to guide resource reservation in a distributed setting in order to simultaneously improve utilization and efficiency by reducing resource contention and saturation.

Reception to follow on TUESDAY, April 30, 2019 at 12:00pm in Wegmans Hall 3rd Floor Atrium

Advisor: Prof. Sandhya Dwarkadas (Computer Science)

Committee: Prof. Michael Scott (Computer Science), Prof. Michael Huang (Electrical & Computer Engineering), Dr. Kai Shen (Google)