Biblio

Software systems are increasingly called upon to autonomously manage their goals in changing contexts and environments, and under evolving requirements. In some circumstances, autonomous systems cannot be fully-automated but instead cooperate with human operators to maintain and adapt themselves. Furthermore, there are times when a choice should be made between doing a manual or automated repair. Involving operators in self-adaptation should itself be adaptive, and consider aspects such as the training, attention, and ability of operators. Not only do these aspects change from person to person, but they may change with the same person. These aspects make the choice of whether to involve humans non-obvious. Self-adaptive systems should trade-off whether to involve operators, taking these aspects into consideration along with other business qualities it is attempting to achieve. In this chapter, we identify the various roles that operators can perform in cooperating with self-adapting systems. We focus on humans as effectors-doing tasks which are difficult or infeasible to automate. We describe how we modified our self-adaptive framework, Rainbow, to involve operators in this way, which involved choosing suitable human models and integrating them into the existing utility trade-off decision models of Rainbow. We use probabilistic modeling and quantitative verification to analyze the trade-offs of involving humans in adaptation, and complement our study with experiments to show how different business preferences and modalities of human involvement may result in different outcomes.

In an organization, the interactions users have with software leave patterns or traces of the parts of the systems accessed. These interactions can be associated with the underlying software architecture. The first step in detecting problems like insider threat is to detect those traces that are anomalous. Here, we propose a method to find anomalous users leveraging these interaction traces, categorized by user roles. We propose a model based approach to cluster user sequences and find outliers. We show that the approach works on a simulation of a large scale system based on and Amazon Web application style.

We propose an interactive approach where analysts reason about the security of a system using an abstraction of its runtime structure, as opposed to looking at the code. They interactively refine a hierarchical object graph, set security properties on abstract objects or edges, query the graph, and investigate the results by studying highlighted objects or edges or tracing to the code. Behind the scenes, an inference analysis and an extraction analysis maintain the soundness of the graph with respect to the code.

Conventional security mechanisms at network, host, and source code levels are no longer sufficient in detecting and responding to increasingly dynamic and sophisticated cyber threats today. Detecting anomalous behavior at the architectural level can help better explain the intent of the threat and strengthen overall system security posture. To that end, we present a framework that mines software component interactions from system execution history and applies a detection algorithm to identify anomalous behavior. The framework uses unsupervised learning at runtime, can perform fast anomaly detection “on the fly”, and can quickly adapt to system load fluctuations and user behavior shifts. Our evaluation of the approach against a real Emergency Deployment System has demonstrated very promising results, showing the framework can effectively detect covert attacks, including insider threats, that may be easily missed by traditional intrusion detection methods. 

Modern frameworks are required to be extendable as well as secure. However, these two qualities are often at odds. In this poster we describe an approach that uses a combination of static analysis and run-time management, based on software architecture models, that can improve security while maintaining framework extendability. We implement a prototype of the approach for the Android platform. Static analysis identifies the architecture and communication patterns among the collection of apps on an Android device and which communications might be vulnerable to attack. Run-time mechanisms monitor these potentially vulnerable communication patterns, and adapt the system to either deny them, request explicit approval from the user, or allow them.

It is more expensive and time consuming to build modern software without extensive supply chains. Supply chains decrease these development risks, but typically at the cost of increased security risk. In particular, it is often difficult to understand or verify what a software component delivered by a third party does or could do. Such a component could contain unwanted behaviors, vulnerabilities, or malicious code, many of which become incorporated in applications utilizing the component. Sandboxes provide relief by encapsulating a component and imposing a security policy on it. This limits the operations the component can perform without as much need to trust or verify the component. Instead, a component user must trust or verify the relatively simple sandbox. Given this appealing prospect, researchers have spent the last few decades developing new sandboxing techniques and sandboxes. However, while sandboxes have been adopted in practice, they are not as pervasive as they could be. Why are sandboxes not achieving ubiquity at the same rate as extensive supply chains? This thesis advances our understanding of and overcomes some barriers to sandbox adoption. We systematically analyze ten years (2004 – 2014) of sandboxing research from top-tier security and systems conferences. We uncover two barriers: (1) sandboxes are often validated using relatively subjective techniques and (2) usability for sandbox deployers is often ignored by the studied community. We then focus on the Java sandbox to empirically study its use within the open source community. We find features in the sandbox that benign applications do not use, which have promoted a thriving exploit landscape. We develop run time monitors for the Java Virtual Machine (JVM) to turn off these features, stopping all known sandbox escaping JVM exploits without breaking benign applications. Furthermore, we find that the sandbox contains a high degree of complexity benign applications need that hampers sandbox use. When studying the sandbox’s use, we did not find a single application that successfully deployed the sandbox for security purposes, which motivated us to overcome benignly-used complexity via tooling. We develop and evaluate a series of tools to automate the most complex tasks, which currently require error-prone manual effort. Our tools help users derive, express, and refine a security policy and impose it on targeted Java application JARs and classes. This tooling is evaluated through case studies with industrial collaborators where we sandbox components that were previously difficult to sandbox securely. Finally, we observe that design and implementation complexity causes sandbox developers to accidentally create vulnerable sandboxes. Thus, we develop and evaluate a sandboxing technique that leverages existing cloud computing environments to execute untrusted computations. Malicious outcomes produced by the computations are contained by ephemeral virtual machines. We describe a field trial using this technique with Adobe Reader and compare the new sandbox to existing sandboxes using a qualitative framework we developed.

Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

Software developers use #ifdef statements to support code configurability, allowing software product diversification. But because functions can be in many executions paths that depend on complex combinations of configuration options, the introduction of an #ifdef for a given purpose (such as adding a new feature to a program) can enable unintended function calls, which can be a source of vulnerabilities. Part of the difficulty lies in maintaining mental models of all dependencies. We propose analytic visualizations of thevariational callgraph to capture dependencies across configurations and create visualizations to demonstrate how it would help developers visually reason through the implications of diversification, for example through visually doing change impact analysis.

We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions.

Norms provide a way to model the social architecture of a sociotechnical system (STS) and are thus crucial for understanding how such a system supports secure collaboration between principals,that is, autonomous parties such as humans and organizations. Accordingly, an important challenge is to compute the state of a norm instance at runtime in a sociotechnical system.

Custard addresses this challenge by providing a relational syntax for schemas of important norm types along with their canonical lifecycles and providing a mapping from each schema to queries that compute instances of the schema in different lifecycle stages.  In essence, Custard supports a norm-based abstraction layer over underlying information stores such as databases and event logs. Specifically, it supports deadlines; complex events, including those based on aggregation; and norms that reference other norms.

We prove important correctness properties for Custard, including stability (once an event has occurred, it has occurred forever) and safety (a query returns a finite set of tuples).  Our compiler generates SQL queries from Custard specifications.  Writing out such SQL queries by hand is tedious and error-prone even for simple norms, thus demonstrating Custard's practical benefits.

Secure collaboration requires the collaborating parties to apply the
right policies for their interaction.  We adopt a notion of
conditional, directed norms as a way to capture the standards of
correctness for a collaboration.  How can we handle conflicting norms?
We describe an approach based on knowledge of what norm dominates what
norm in what situation.  Our approach adapts answer-set programming to
compute stable sets of norms with respect to their computed conflicts
and dominance.  It assesses agent compliance with respect to those
stable sets.  We demonstrate our approach on a healthcare scenario.