Biblio

Software architects design systems to achieve quality attributes like security, reliability, and performance. Key to achieving these quality attributes are design constraints governing how components of the system are configured, communicate and access resources. Unfortunately, identifying, specifying, communicating and enforcing important design constraints – achieving architectural control – can be difficult, particularly in large software systems. We argue for the development of architectural frameworks, built to leverage language mechanisms that provide for domain-specific syntax, editor services and explicit control over capabilities, that help increase architectural control. In particular, we argue for concise, centralized architectural descriptions which are responsible for specifying constraints and passing a minimal set of capabilities to downstream system components, or explicitly entrusting them to individuals playing defined roles within a team. By integrating these architectural descriptions directly into the language, the type system can help enforce technical constraints and editor services can help enforce social constraints. We sketch our approach in the context of distributed systems. 

Pop-refinement is an approach to stepwise refinement, carried out inside an interactive theorem prover by constructing a monotonically decreasing sequence of predicates over deeply embedded target programs. The sequence starts with a predicate that characterizes the possible implementations, and ends with a predicate that characterizes a unique program in explicit syntactic form. Pop-refinement enables more requirements (e.g. program-level and non-functional) to be captured in the initial specification and preserved through refinement. Security requirements expressed as hyperproperties (i.e. predicates over sets of traces) are always preserved by pop-refinement, unlike the popular notion of refinement as trace set inclusion. Two simple examples in Isabelle/HOL are presented, featuring program-level requirements, non-functional requirements, and hyperproperties.

Much of the data researchers usually collect about users’ privacy and security behavior comes from short-term studies and focuses on specific, narrow activities. We present a design architecture for the Security Behavior Observatory (SBO), a client-server infrastructure designed to collect a wide array of data on user and computer behavior from a panel of hundreds of participants over several years. The SBO infrastructure had to be carefully designed to fulfill several requirements. First, the SBO must scale with the desired length, breadth, and depth of data collection. Second, we must take extraordinary care to ensure the security and privacy of the collected data, which will inevitably include intimate details about our participants’ behavior. Third, the SBO must serve our research interests, which will inevitably change over the course of the study, as collected data is analyzed, interpreted, and suggest further lines of inquiry. We describe in detail the SBO infrastructure, its secure data collection methods, the benefits of our design and implementation, as well as the hurdles and tradeoffs to consider when designing such a data collection system. 

Research shows that commonly accepted security requirements   are  not  generally  applied  in  practice.  Instead  of relying on requirements checklists, security experts rely on their expertise and background knowledge to identify security vulnerabilities.  To  understand  the  gap  between  available checklists  and  practice,  we  conducted  a  series  of  interviews  to encode   the   decision-making   process   of  security   experts   and novices during security requirements analysis. Participants were asked to analyze two types of artifacts: source code, and network diagrams  for  vulnerabilities  and  to  apply  a  requirements checklist to mitigate some of those vulnerabilities.  We framed our study using Situation Awareness—a cognitive theory from psychology—to   elicit  responses   that  we  later  analyzed   using coding theory and grounded analysis.  We report our preliminary results of analyzing two interviews that reveal possible decision- making patterns that could characterize how analysts perceive, comprehend   and  project  future  threats  which  leads  them  to decide upon requirements  and their specifications,  in addition, to how  experts  use  assumptions  to  overcome  ambiguity  in specifications.  Our goal is to build a model that researchers  can use to evaluate their security requirements methods against how experts transition through different situation awareness levels in their decision-making  process.

Security requirements patterns represent reusable security practices that software engineers can apply to improve security in their system. Reusing best practices that others have employed could have a number of benefits, such as decreasing the time spent in the requirements elicitation process or improving the quality of the product by reducing product failure risk. Pattern selection can be difficult due to the diversity of applicable patterns from which an analyst has to choose. The challenge is that identifying the most appropriate pattern for a situation can be cumbersome and time-consuming. We propose a new method that combines an inquiry-cycle based approach with the feature diagram notation to review only relevant patterns and quickly select the most appropriate patterns for the situation. Similar to patterns themselves, our approach captures expert knowledge to relate patterns based on decisions made by the pattern user. The resulting pattern hierarchies allow users to be guided through these decisions by questions, which introduce related patterns in order to help the pattern user select the most appropriate patterns for their situation, thus resulting in better requirement generation. We evaluate our approach using access control patterns in a pattern user study.

The use of shared mutable state, commonly seen in object-oriented systems, is often problematic due to the potential conflicting interactions between aliases to the same state. We present a substructural type system outfitted with a novel lightweight interference control mechanism, rely-guarantee protocols, that enables controlled aliasing of shared resources. By assigning each alias separate roles, encoded in a novel protocol abstraction in the spirit of rely-guarantee reasoning, our type system ensures that challenging uses of shared state will never interfere in an unsafe fashion. In particular, rely-guarantee protocols ensure that each alias will never observe an unexpected value, or type, when inspecting shared memory regardless of how the changes to that shared state (originating from potentially unknown program contexts) are interleaved at run-time.

Programming languages often include specialized syntax for common datatypes e.g. lists and some also build in support for specific specialized datatypes e.g. regular expressions, but user-defined types must use general-purpose syntax. Frustration with this causes developers to use strings, rather than structured data, with alarming frequency, leading to correctness, performance, security, and usability issues. Allowing library providers to modularly extend a language with new syntax could help address these issues. Unfortunately, prior mechanisms either limit expressiveness or are not safely composable: individually unambiguous extensions can still cause ambiguities when used together. We introduce type-specific languages TSLs: logic associated with a type that determines how the bodies of generic literals, able to contain arbitrary syntax, are parsed and elaborated, hygienically. The TSL for a type is invoked only when a literal appears where a term of that type is expected, guaranteeing non-interference. We give evidence supporting the applicability of this approach and formally specify it with a bidirectionally typed elaboration semantics for the Wyvern programming language.

Application Programming Interfaces APIs often define object protocols. Objects with protocols have a finite number of states and in each state a different set of method calls is valid. Many researchers have developed protocol verification tools because protocols are notoriously difficult to follow correctly. However, recent research suggests that a major challenge for API protocol programmers is effectively searching the state space. Verification is an ineffective guide for this kind of search. In this paper we instead propose Plaiddoc, which is like Javadoc except it organizes methods by state instead of by class and it includes explicit state transitions, state-based type specifications, and rich state relationships. We compare Plaiddoc to a Javadoc control in a between-subjects laboratory experiment. We find that Plaiddoc participants complete state search tasks in significantly less time and with significantly fewer errors than Javadoc participants.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram that shows one box for a class to represent all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram, where one box for a class represents all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure, to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.

Phishing is a social engineering tactic that targets internet users in an attempt to trick them into divulging personal information. When opening an email, users are faced with the decision of determining if an email is legitimate or an attempt at phishing. Although software has been developed to assist the user, studies have shown they are not foolproof, leaving the user vulnerable. Multiple training programs have been developed to educate users in their efforts to make informed decisions; however, training that conveys the real world consequences of phishing or training that increases a user’s fear level have not been developed. Conveying real world consequences of a situation and increasing a user’s fear level have been proven to enhance the effects of training in other fields. Ninety-six participants were recruited and randomly assigned to training programs with phishing consequences, training programs designed to increase fear, or a control group. Preliminary results indicate that training helped users identify phishing emails; however, little difference was seen among the three groups. Future analysis will include a factor analysis of personality and individual differences that influence training efficacy.

Static types may be used both by the language implementation and directly by the user as documentation. Though much existing work focuses primarily on the implications of static types on the semantics of programs, relatively little work considers the impact on usability that static types provide. Though the omission of static type information may decrease program length and thereby improve readability, it may also decrease readability because users must then frequently derive type information manually while reading programs. As type inference becomes more popular in languages that are in widespread use, it is important to consider whether the adoption of type inference may impact productivity of developers.

Complex software systems are becoming increasingly prevalent in aerospace applications: in particular, to
accomplish critical tasks. Ensuring the safety of these systems is crucial, as they can have subtly different behaviors
under slight variations in operating conditions. This paper advocates the use of formal verification techniques and in
particular theorem proving for hybrid software-intensive systems as a well-founded complementary approach to the
classical aerospace verification and validation techniques, such as testing or simulation. As an illustration of these
techniques, a novel lateral midair collision-avoidance maneuver is studied in an ideal setting, without accounting for
the uncertainties of the physical reality. The challenges that naturally arise when applying such technology to
industrial-scale applications is then detailed, and proposals are given on how to address these issues.

Web applications must ultimately command systems like web browsers and database engines using strings. Strings derived from improperly sanitized user input can as a result be a vector for command injection attacks. In this paper, we introduce regular string types, which classify strings constrained statically to be in a regular language specified by a regular expression. Regular strings support standard string operations like concatenation and substitution, as well as safe coercions, so they can be used to implement, in an essentially conventional manner, the pieces of a web application or framework that handle strings arising from user input. Simple type annotations at function interfaces can be used to statically verify that sanitization has been performed correctly without introducing redundant run-time checks. We specify this type system first as a minimal typed lambda calculus, lambdaRS. To be practical, adopting a specialized type system like this should not require the adoption of a new programming language. Instead, we advocate for extensible type systems: new type system fragments like this should be implemented as libraries atop a mechanism that guarantees that they can be safely composed. We support this with two contributions. First, we specify a translation from lambdaRS to a calculus with only standard strings and regular expressions. Then, taking Python as a language with these constructs, we implement the type system together with the translation as a library using typy, an extensible static type system for Python.

Breaches of software security affect millions of people, and therefore it is crucial to strive for more secure software systems. However, the effect of programming language design on software security is not easily measured or studied. In the absence of scientific insight, opinions range from those that claim that programming language design has no effect on security of the system, to those that believe that programming language design is the only way to provide “high-assurance software.” In this paper, we discuss how programming language design can impact software security by looking at a specific example: the Wyvern programming language. We report on how the design of the Wyvern programming language leverages security principles, together with hypotheses about how usability impacts security, in order to prevent command injection attacks. Furthermore, we discuss what security principles we considered in Wyvern’s design. 

State estimation plays a critically important role in ensuring the secure and reliable operation of the electric grid. Recent works have shown that the state estimation process is vulnerable to stealthy attacks where an adversary can alter certain measurements to corrupt the solution of the process, but evade the existing bad data detection algorithms and remain invisible to the system operator. Since the state estimation result is used to compute optimal power flow and perform contingency analysis, incorrect estimation can undermine economic and secure system operation. However, an adversary needs sufficient resources as well as necessary knowledge to achieve a desired attack outcome. The knowledge that is required to launch an attack mainly includes the measurements considered in state estimation, the connectivity among the buses, and the power line admittances. Uncertainty in information limits the potential attack space for an attacker. This advantage of uncertainty enables us to apply moving target defense (MTD) strategies for developing a proactive defense mechanism for state estimation.

In this paper, we propose an MTD mechanism for securing state estimation, which has several characteristics: (i) increase the knowledge uncertainty for attackers, (ii) reduce the window of attack opportunity, and (iii) increase the attack cost. In this mechanism, we apply controlled randomization on the power grid system properties, mainly on the set of measurements that are considered in state estimation, and the topology, especially the line admittances. We thoroughly analyze the performance of the proposed mechanism on the standard IEEE 14- and 30-bus test systems.

We investigate strong normalization, confluence, and behavioral equality in the realm of session-based concurrency. These interrelated issues underpin advanced correctness analysis in models of structured communications. The starting point for our study is an interpretation of linear logic propositions as session types for communicating processes, proposed in prior work. Strong normalization and confluence are established by developing a theory of logical relations. Defined upon a linear type structure, our logical relations remain remarkably similar to those for functional languages. We also introduce a natural notion of observational equivalence for session-typed processes. Strong normalization and confluence come in handy in the associated coinductive reasoning: as applications, we prove that all proof conversions induced by the logic interpretation actually express observational equivalences, and explain how type isomorphismsresulting from linear logic equivalences are realized by coercions between interface types of session-based concurrent systems.

Growing traffic volumes and the increasing complexity of attacks pose a constant scaling challenge for network intrusion prevention systems (NIPS). In this respect, offloading NIPS processing to compute clusters offers an immediately deployable alternative to expensive hardware upgrades. In practice, however, NIPS offloading is challenging on three fronts in contrast to passive network security functions: (1) NIPS offloading can impact other traffic engineering objectives; (2) NIPS offloading impacts user perceived latency; and (3) NIPS actively change traffic volumes by dropping unwanted traffic. To address these challenges, we present the SNIPS system. We design a formal optimization framework that captures tradeoffs across scalability, network load, and latency. We provide a practical implementation using recent advances in software-defined networking without requiring modifications to NIPS hardware. Our evaluations on realistic topologies show that SNIPS can reduce the maximum load by up to 10× while only increasing the latency by 2%.

In our previous work we showed that for Fedora, under normal operational conditions, security problem discovery appears to be a random process. While in the case of Fedora, and a number of other open source products, classical reliability models can be adapted to estimate the number of residual security problems under “normal” operational usage (not attacks), the predictive ability of the model is lower for security faults due to the rarity of security events and because there appears to be no real security reliability growth. The ratio of security to non-security faults is an indicator that the process needs improving, but it also may be leveraged to assess vulnerability profile of a release and possibly guide testing of its next version. We manually analyzed randomly sampled problems for four different versions of Fedora and classified them into security vulnerability categories. We also analyzed the distribution of these problems over the software’s lifespan and we found that they exhibit a symmetry which can perhaps be used in estimating the number of residual security problems in the software. Based on our work, we believe that an approach to vulnerability elimination based on a combination of “classical” and some non-operational “bounded” high-assurance testing along the lines discussed in may yield good vulnerability elimination results, as well as a way of estimating vulnerability level of a release. Classical SRE methods, metrics and models can be used to track both non-security and security problem detection under normal operational profile. We can then model the reliability growth, if any, and estimate the number of residual faults by estimating the lower and upper bounds on the total number of faults of a certain type. In production, there may be a simpler alternative. Just count the vulnerabilities and project over the next period assuming constant vulnerability discovery rate. In testing phase, to accelerate the process, one might leverage collected vulnerability information to generate non-operational test-cases aimed at vulnerability categories. The observed distributions of security problems reported under normal “operational” usage appear to support the above approach – i.e., what is learned say in the first x weeks can them be leveraged in selecting test cases in the next stage. Similarly, what is learned about a product Y weeks after its release may be very indicative of its vulnerability profile for the rest of its life given the assumption of constant vulnerability discovery rate.

Identifying factors behind countries’ weakness to cyber-attacks is an important step towards addressing these weaknesses at the root level.  For example, identifying factors why some countries become cyber- crime safe heavens can inform policy actions about how to reduce the attractiveness of these countries to cyber-criminals.  Currently, however, identifying these factors is mostly based on expert opinions and speculations.

In this work, we perform an empirical study to statistically test the validity of these opinions and specu- lations.  In our analysis, we use Symantec’s World Intelligence Network Environment (WINE) Intrusion Prevention System (IPS) telemetry data which contain attack reports from more than 10 million customer computers worldwide.  We use regression analysis to test for the relevance of multiple factors including monetary and computing resources, cyber-security research and institutions, and corruption.

Our analysis confirms some hypotheses and disproves others. We find that many countries in Eastern Europe extensively host attacking computers because of a combination of good computing infrastructure and high corruption rate.  We also find that web attacks and fake applications are most prevalent in rich countries because attacks on these countries are more lucrative. Finally, we find that computers in Africa launch the lowest rates of cyber-attacks. This is surprising given the bad cyber reputation of some African countries such as Nigeria. Our research has many policy implications.

Software as a Service (SaaS) is the most prevalent service delivery mode for cloud systems. This paper surveys common security vulnerabilities and corresponding countermeasures for SaaS. It is primarily focused on the work published in the last five years. We observe current SaaS security trends and a lack of sufficiently broad and robust countermeasures in some of the SaaS security area such as Identity and Access management due to the growth of SaaS applications.

Software as a Service (SaaS) is the most prevalent service delivery mode for cloud systems. This paper surveys common security vulnerabilities and corresponding countermeasures for SaaS. It is primarily focused on the work published in the last five years. We observe current SaaS security trends and a lack of sufficiently broad and robust countermeasures in some of the SaaS security area such as Identity and Access management due to the growth of SaaS applications.

The process of software development and evolution has proven difficult to improve. For example,  well documented security issues such as SQL injection (SQLi), after more than a decade, still top  most vulnerability lists. Quantitative security process and quality metrics are often subdued due to  lack of time and resources. Security problems are hard to quantify and even harder to predict or  relate to any process improvement activity.  The goal of this thesis is to assess usefulness of “classical” software reliability engineering (SRE)  models in the context of open source software security, the conditions under which they may be  useful, and the information that they can provide with respect to the security quality of a software  product.  We start with security problem reports for open source Fedora series of software releases.We  illustrate how one can learn from normal operational profile about the non-operational processes  related to security problems. One aspect is classification of security problems based on the human  traits that contribute to the injection of problems into code, whether due to poor practices or limited  knowledge (epistemic errors), or due to random accidental events (aleatoric errors). Knowing the  distribution aids in development of an attack profile. In the case of Fedora, the distribution of  security problems found post-release was consistent across four different releases of the software.  The security problem discovery rate appears to be roughly constant but much lower than the initial  non-security problem discovery rate. Previous work has shown that non-operational testing can help  accelerate and focus the problem discovery rate and that it can be successfully modeled.We find  that some classical reliability models can be used with success to estimate the residual number of  security problems, and through that provide a measure of the security characteristics of the software.  We propose an agile software testing process that combines operational and non-operational (or  attack related) testing with the intent of finding more security problems faster.