Biblio

The ever increasing expansion of mobile applications into nearly every aspect of modern life, from banking to healthcare systems, is making their security more important than ever. Modern smartphone operating systems (OS) rely substantially on the permission-based security model to enforce restrictions on the operations that each application can perform. In this paper, we perform an analysis of the permission protocol implemented in Android, a popular OS for smartphones. We propose a formal model of the Android permission protocol in Alloy, and describe a fully automatic analysis that identifies potential flaws in the protocol. A study of real-world Android applications corroborates our finding that the flaws in the Android permission protocol can have severe security implications, in some cases allowing the attacker to bypass the permission checks entirely.

Android is the most popular platform for mobile devices. It facilitates sharing of data and services among applications using a rich inter-app communication system. While access to resources can be controlled by the Android permission system, enforcing permissions is not sufficient to prevent security violations, as permissions may be mismanaged, intentionally or unintentionally. Android's enforcement of the permissions is at the level of individual apps, allowing multiple malicious apps to collude and combine their permissions or to trick vulnerable apps to perform actions on their behalf that are beyond their individual privileges. In this paper, we present COVERT, a tool for compositional analysis of Android inter-app vulnerabilities. COVERT's analysis is modular to enable incremental analysis of applications as they are installed, updated, and removed. It statically analyzes the reverse engineered source code of each individual app, and extracts relevant security specifications in a format suitable for formal verification. Given a collection of specifications extracted in this way, a formal analysis engine (e.g., model checker) is then used to verify whether it is safe for a combination of applications-holding certain permissions and potentially interacting with each other-to be installed together. Our experience with using COVERT to examine over 500 real-world apps corroborates its ability to find inter-app vulnerabilities in bundles of some of the most popular apps on the market.

As the dominant mobile computing platform, Android has become a prime target for cyber-security attacks. Many of these attacks are manifested at the application level, and through the exploitation of vulnerabilities in apps downloaded from the popular app stores. Increasingly, sophisticated attacks exploit the vulnerabilities in multiple installed apps, making it extremely difficult to foresee such attacks, as neither the app developers nor the store operators know a priori which apps will be installed together. This paper presents an approach that allows the end-users to safeguard a given bundle of apps installed on their device from such attacks. The approach, realized in a tool, called SEPAR, combines static analysis with lightweight formal methods to automatically infer security-relevant properties from a bundle of apps. It then uses a constraint solver to synthesize possible security exploits, from which fine-grained security policies are derived and automatically enforced to protect a given device. In our experiments with over 4,000 Android apps, SEPAR has proven to be highly effective at detecting previously unknown vulnerabilities as well as preventing their exploitation.

The Alloy specification language, and the corresponding Alloy Analyzer, have received much attention in the last two decades with applications in many areas of software engineering. Increasingly, formal analyses enabled by Alloy are desired for use in an on-line mode, where the specifications are automatically kept in sync with the running, possibly changing, software system. However, given Alloy Analyzer’s reliance on computationally expensive SAT solvers, an important challenge is the time it takes for such analyses to execute at runtime. The fact that in an on-line mode, the analyses are often repeated on slightly revised versions of a given specification, presents us with an opportunity to tackle this challenge. We present Titanium, an extension of Alloy for formal analysis of evolving specifications. By leveraging the results from previous analyses, Titanium narrows the state space of the revised specification, thereby greatly reducing the required computational effort. We describe the semantic basis of Titanium in terms of models specified in relational logic. We show how the approach can be realized atop an existing relational logic model finder. Our experimental results show Titanium achieves a significant speed-up over Alloy Analyzer when applied to the analysis of evolving specifications.

Security analysis requires some degree of knowledge to align threats to vulnerabilities in information technology. Despite the abundance of security requirements, the evidence suggests that security experts are not applying these checklists. Instead, they default to their background knowledge to identify security vulnerabilities. To better understand the different effects of security checklists, analysis and expertise, we conducted a series of interviews to capture and encode the decisionmaking process of security experts and novices during three security requirements analysis exercises. Participants were asked to analyze three kinds of artifacts: source code, data flow diagrams, and network diagrams, for vulnerabilities, and then to apply a requirements checklist to demonstrate their ability to mitigate vulnerabilities. We framed our study using Situation Awareness theory to elicit responses that were analyzed using coding theory and grounded analysis. Our results include decision-making patterns that characterize how analysts perceive, comprehend and project future threats, and how these patterns relate to selecting security mitigations. Based on this analysis, we discovered new theory to measure how security experts and novices apply attack models and how structured and unstructured analysis enables increasing security requirements coverage. We discuss suggestions of how our method could be adapted and applied to improve training and education instruments of security analysts.

Choosing how to write natural language scenarios is challenging, because stakeholders may over-generalize their descriptions or overlook or be unaware of alternate scenarios. In security, for example, this can result in weak security constraints that are too general, or missing constraints. Another challenge is that analysts are unclear on where to stop generating new scenarios. In this paper, we introduce the Multifactor Quality Method (MQM) to help requirements analysts to empirically collect system constraints in scenarios based on elicited expert preferences. The method combines quantitative statistical analysis to measure system quality with qualitative coding to extract new requirements. The method is bootstrapped with minimal analyst expertise in the domain affected by the quality area, and then guides an analyst toward selecting expert-recommended requirements to monotonically increase system quality. We report the results of applying the method to security. This include 550 requirements elicited from 69 security experts during a bootstrapping stage, and subsequent evaluation of these results in a verification stage with 45 security experts to measure the overall improvement of the new requirements. Security experts in our studies have an average of 10 years of experience. Our results show that using our method, we detect an increase in the security quality ratings collected in the verification stage. Finally, we discuss how our proposed method helps to improve security requirements elicitation, analysis, and measurement. 

Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of distributed knowledge, we investigate the challenge of developing a security requirements rule base that mimics human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules underpinning the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The proposed approach is tested by evaluating 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The initial results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings. 

As information security became an increasing concern for software developers and users, requirements engineering (RE) researchers brought new insight to security requirements. Security requirements aim to address security at the early stages of system design while accommodating the complex needs of different stakeholders. Meanwhile, other research communities, such as usable privacy and security, have also examined these requirements with specialized goal to make security more usable for stakeholders from product owners, to system users and administrators. In this paper we report results from conducting a literature survey to compare security requirements research from RE Conferences with the Symposium on Usable Privacy and Security (SOUPS). We report similarities between the two research areas, such as common goals, technical definitions, research problems, and directions. Further, we clarify the differences between these two communities to understand how they can leverage each other’s insights. From our analysis, we recommend new directions in security requirements research mainly to expand the meaning of security requirements in RE to reflect the technological advancements that the broader field of security is experiencing. These recommendations to encourage crosscollaboration with other communities are not limited to the security requirements area; in fact, we believe they can be generalized to other areas of RE. 

Security analysis requires specialized knowledge to align threats and vulnerabilities in information technology. To identify mitigations, analysts need to understand how threats, vulnerabilities, and mitigations are composed together to yield security requirements. Despite abundant guidance in the form of checklists and controls about how to secure systems, evidence suggests that security experts do not apply these checklists. Instead, they rely on their prior knowledge and experience to identify security vulnerabilities. To better understand the different effects of checklists, design analysis, and expertise, we conducted a series of interviews to capture and encode the decisionmaking process of security experts and novices during three security analysis exercises. Participants were asked to analyze three kinds of artifacts: source code, data flow diagrams, and network diagrams, for vulnerabilities, and then to apply a requirements checklist to demonstrate their ability to mitigate vulnerabilities. We framed our study using Situation Awareness, which is a theory about human perception that was used to elicit interviewee responses. The responses were then analyzed using coding theory and grounded analysis. Our results include decision-making patterns that characterize how analysts perceive, comprehend, and project future threats against a system, and how these patterns relate to selecting security mitigations. Based on this analysis, we discovered new theory to measure how security experts and novices apply attack models and how structured and unstructured analysis enables increasing security requirements coverage. We highlight the role of expertise level and requirements composition in affecting security decision-making and we discuss how our method produced new hypotheses about security analysis and decisionmaking.

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 analysis depends on how well-trained analysts perceive security risk, understand the impact of various vulnerabilities, and mitigate threats. When systems are composed of multiple machines, configurations, and software components that interact with each other, risk perception must account for the composition of security requirements. In this paper, we report on how changes to security requirements affect analysts risk perceptions and their decisions about how to modify the requirements to reach adequate security levels. We conducted two user surveys of 174 participants wherein participants assess security levels across 64 factorial vignettes. We analyzed the survey results using multi-level modeling to test for the effect of security requirements composition on participants’ overall security adequacy ratings and on their ratings of individual requirements. We accompanied this analysis with grounded analysis of elicited requirements aimed at lowering the security risk. Our results suggest that requirements composition affects experts’ adequacy ratings on security requirements. In addition, we identified three categories of requirements modifications, called refinements, replacements and reinforcements, and we measured how these categories compare with overall perceived security risk. Finally, we discuss the future impact of our work in security requirements assessment practice.

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.

Insider threats are a well-known problem, and previous studies have shown that it has a huge impact over a wide range of sectors like financial services, governments, critical infrastructure services and the telecommunications sector. Users, while interacting with any software system, leave a trace of what nodes they accessed and in what sequence. We propose to translate these sequences of observed activities into paths on the graph of the underlying software architectural model. We propose a clustering algorithm to find anomalies in the data, which can be combined with contextual information to confirm as an insider threat.

Large software systems have to contend with a significant number of users who interact with different components of the system in various ways. The sequences of components that are used as part of an interaction define sets of behaviors that users have with the system. These can be large in number. Among these users, it is possible that there are some who exhibit anomalous behaviors -- for example, they may have found back doors into the system and are doing something malicious. These anomalous behaviors can be hard to distinguish from normal behavior because of the number of interactions a system may have, or because traces may deviate only slightly from normal behavior. In this paper we describe a model-based approach to cluster sequences of user behaviors within a system and to find suspicious, or anomalous, sequences. We exploit the underlying software architecture of a system to define these sequences. We further show that our approach is better at detecting suspicious activities than other approaches, specifically those that use unigrams and bigrams for anomaly detection. We show this on a simulation of a large scale system based on Amazon Web application style architecture.

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.

As information security became an increasing
concern for software developers and users, requirements
engineering (RE) researchers brought new insight to security
requirements. Security requirements aim to address security at
the early stages of system design while accommodating the
complex needs of different stakeholders. Meanwhile, other
research communities, such as usable privacy and security,
have also examined these requirements with specialized goal to
make security more usable for stakeholders from product
owners, to system users and administrators. In this paper we
report results from conducting a literature survey to compare
security requirements research from RE Conferences with the
Symposium on Usable Privacy and Security (SOUPS). We
report similarities between the two research areas, such as
common goals, technical definitions, research problems, and
directions. Further, we clarify the differences between these
two communities to understand how they can leverage each
other’s insights. From our analysis, we recommend new
directions in security requirements research mainly to expand
the meaning of security requirements in RE to reflect the
technological advancements that the broader field of security is
experiencing. These recommendations to encourage crosscollaboration
with other communities are not limited to the
security requirements area; in fact, we believe they can be
generalized to other areas of RE.

A fundamental problem in the specification of regulatory privacy policies such as the Health Insurance Portability and Accountability Act (HIPAA) in a computer system is to state the policies precisely, consistent with their high-level intuition. In this paper, we propose UML sequence diagrams as a practical means to graphically express privacy policies. A graphical representation allows decision-makers such as application domain experts and security architects to easily verify and confirm the expected behavior. Once intuitively confirmed, our work in this article introduces an algorithmic approach to formalizing the semantics of sequence diagrams in terms of linear temporal logic (LTL) templates. In all the templates, different semantic aspects are expressed as separate, yet simple LTL formulas that can be composed to define the complex semantics of sequence diagrams. The formalization enables us to leverage the analytical powers of automated decision procedures for LTL formulas to determine if a collection of sequence diagrams is consistent, independent, etc. and also to verify if a system design conforms to the privacy policies. We evaluate our approach by modeling and analyzing a substantial subset of HIPAA rules using sequence diagrams.