Biblio

This paper describes the process of developing data visualisations to enhance a commercial software platform for combating insider threat, whose existing UI, while perfectly functional, was limited in its ability to allow analysts to easily spot the patterns and outliers that visualisation naturally reveals. We describe the design and development process, proceeding from initial tasks/requirements gathering, understanding the platform’s data formats, the rationale behind the visualisations’ design, and then refining the prototype through gathering feedback from representative domain experts who are also current users of the software. Through a number of example scenarios, we show that the visualisation can support the identified tasks and aid analysts in discovering and understanding potentially risky insider activity within a large user base.

The human factor is often regarded as the weakest link in cybersecurity systems. The investigation of several security breaches reveals an important impact of human errors in exhibiting security vulnerabilities. Although security researchers have long observed the impact of human behavior, few improvements have been made in designing secure systems that are resilient to the uncertainties of the human element.

In this talk, we discuss several psychological theories that attempt to understand and influence the human behavior in the cyber world. Our goal is to use such theories in order to build predictive cyber security models that include the behavior of typical users, as well as system administrators. We then illustrate the importance of our approach by presenting a case study that incorporates models of human users. We analyze our preliminary results and discuss their challenges and our approaches to address them in the future.

In this paper we explore the differential perceptions of cybersecurity professionals and general users regarding access rules and passwords. We conducted a preliminary survey involving 28 participants: 15 cybersecurity professionasl and 13 general users. We present our preliminary findings and explain how such survey data might be used to improve security in practice. We focus on user fatigue with access rules and passwords.

Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS mes- sages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security- sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security- sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.

In real world domains, from healthcare to power to finance, we deploy computer systems intended to streamline and im- prove the activities of human agents in the corresponding non-cyber worlds. However, talking to actual users (instead of just computer security experts) reveals endemic circum- vention of the computer-embedded rules. Good-intentioned users, trying to get their jobs done, systematically work around security and other controls embedded in their IT systems.

This poster reports on our work compiling a large corpus of such incidents and developing a model based on semi- otic triads to examine security circumvention. This model suggests that mismorphisms—mappings that fail to preserve structure—lie at the heart of circumvention scenarios; dif- ferential perceptions and needs explain users’ actions. We support this claim with empirical data from the corpus.

Agent-based modeling can serve as a valuable asset to security personnel who wish to better understand the security landscape within their organization, especially as it relates to user behavior and circumvention. In this paper, we ar- gue in favor of cognitive behavioral agent-based modeling for usable security, report on our work on developing an agent- based model for a password management scenario, perform a sensitivity analysis, which provides us with valuable insights into improving security (e.g., an organization that wishes to suppress one form of circumvention may want to endorse another), and provide directions for future work.

In real world domains, from healthcare to power to finance, we deploy computer systems intended to streamline and improve the activities of human agents in the corresponding non-cyber worlds. However, talking to actual users (instead of just computer security experts) reveals endemic circumvention of the computer-embedded rules. Good-intentioned users, trying to get their jobs done, systematically work around security and other controls embedded in their IT systems.

This paper reports on our work compiling a large corpus of such incidents and developing a model based on semiotic triads to examine security circumvention. This model suggests that mismorphisms— mappings that fail to preserve structure—lie at the heart of circumvention scenarios; differential percep- tions and needs explain users’ actions. We support this claim with empirical data from the corpus.

Security subsystems are often designed with flawed assumptions arising from system designers' faulty mental models. Designers tend to assume that users behave according to some textbook ideal, and to consider each potential exposure/interface in isolation. However, fieldwork continually shows that even well-intentioned users often depart from this ideal and circumvent controls in order to perform daily work tasks, and that "incorrect" user behaviors can create unexpected links between otherwise "independent" interfaces. When it comes to security features and parameters, designers try to find the choices that optimize security utility–-except these flawed assumptions give rise to an incorrect curve, and lead to choices that actually make security worse, in practice. We propose that improving this situation requires giving designers more accurate models of real user behavior and how it influences aggregate system security. Agent-based modeling can be a fruitful first step here. In this paper, we study a particular instance of this problem, propose user-centric techniques designed to strengthen the security of systems while simultaneously improving the usability of them, and propose further directions of inquiry.

Typing is a human activity that can be affected by a number of situational and task-specific factors. Changes in typing behavior resulting from the manipulation of such factors can be predictably observed through key-level input analytics. Here we present a study designed to explore these relationships. Participants play a typing game in which letter composition, word length and number of words appearing together are varied across levels. Inter-keystroke timings and other higher order statistics (such as bursts and pauses), as well as typing strategies, are analyzed from game logs to find the best set of metrics that quantify the effect that different experimental factors have on observable metrics. Beyond task-specific factors, we also study the effects of habituation by recording changes in performance with practice. Currently a work in progress, this research aims at developing a predictive model of human typing. We believe this insight can lead to the development of novel security proofs for interactive systems that can be deployed on existing infrastructure with minimal overhead. Possible applications of such predictive capabilities include anomalous behavior detection, authentication using typing signatures, bot detection using word challenges etc.

As mobile technology begins to dominate computing, understanding how their use impacts security becomes increasingly important. Fortunately, this challenge is also an opportunity: the rich set of sensors with which most mobile devices are equipped provide a rich contextual dataset, one that should enable mobile user behavior to be modeled well enough to predict when users are likely to act insecurely, and provide cognitively grounded explanations of those behaviors. We will evaluate this hypothesis with a series of experiments designed first to confirm that mobile sensor data can reliably predict user stress, and that users experiencing such stress are more likely to act insecurely.

To keep malware out of mobile application markets, existing techniques analyze the security aspects of application behaviors and summarize patterns of these security aspects to determine what applications do. However, user expectations (reflected via user perception in combination with user judgment) are often not incorporated into such analysis to determine whether application behaviors are within user expectations. This poster presents our recent work on bridging the semantic gap between user perceptions of the application behaviors and the actual application behaviors.

One of the biggest challenges in mobile security is human behavior. The most secure password may be useless if it is sent as a text or in an email. The most secure network is only as secure as its most careless user. Thus, in the current project we sought to discover the conditions under which users of mobile devices were most likely to make security errors. This scaffolds a larger project where we will develop automatic ways of detecting such environments and eventually supporting users during these times to encourage safe mobile behaviors.

We present an 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 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 of the collected data, which will inevitably include intimate participant behavioral data. Third, the SBO must serve our research interests, which will inevitably change as collected data is analyzed and interpreted. This short paper summarizes some of our design and implementation benefits and discusses a few hurdles and trade-offs to consider when designing such a data collection system.

This paper presents a model for generating personalized passwords (i.e., passwords based on user and service profile). A user's password is generated from a list of personalized words, each word is drawn from a topic relating to a user and the service in use. The proposed model can be applied to: (i) assess the strength of a password (i.e., determine how many guesses are used to crack the password), and (ii) generate secure (i.e., contains digits, special characters, or capitalized characters) yet easy to memorize passwords.

We argue that emergent behavior is inherent to cybersecurity.

While automated methods are the first line of defense for detecting attacks on webservers, a human agent is required to understand the attacker's intent and the attack process. The goal of this research is to understand the value of various log fields and the cognitive processes by which log information is grouped, searched, and correlated. Such knowledge will enable the development of human-focused log file investigation technologies. We performed controlled experiments with 65 subjects (IT professionals and novices) who investigated excerpts from six webserver log files. Quantitative and qualitative data were gathered to: 1) analyze subject accuracy in identifying malicious activity; 2) identify the most useful pieces of log file information; and 3) understand the techniques and strategies used by subjects to process the information. Statistically significant effects were observed in the accuracy of identifying attacks and time taken depending on the type of attack. Systematic differences were also observed in the log fields used by high-performing and low-performing groups. The findings include: 1) new insights into how specific log data fields are used to effectively assess potentially malicious activity; 2) obfuscating factors in log data from a human cognitive perspective; and 3) practical implications for tools to support log file investigations.

One hundred-sixty four participants from the United States, India and China completed a survey designed to assess past phishing experiences and whether they engaged in certain online safety practices (e.g., reading a privacy policy). The study investigated participants' reported agreement regarding the characteristics of phishing attacks, types of media where phishing occurs and the consequences of phishing. A multivariate analysis of covariance indicated that there were significant differences in agreement regarding phishing characteristics, phishing consequences and types of media where phishing occurs for these three nationalities. Chronological age and education did not influence the agreement ratings; therefore, the samples were demographically equivalent with regards to these variables. A logistic regression analysis was conducted to analyze the categorical variables and nationality data. Results based on self-report data indicated that (1) Indians were more likely to be phished than Americans, (2) Americans took protective actions more frequently than Indians by destroying old documents, and (3) Americans were more likely to notice the "padlock" security icon than either Indian or Chinese respondents. The potential implications of these results are discussed in terms of designing culturally sensitive anti-phishing solutions.

Information system developers and administrators often overlook critical security requirements and best practices. This may be due to lack of tools and techniques that allow practitioners to tailor security knowledge to their particular context. In order to explore the impact of new security methods, we must improve our ability to study the impact of security tools and methods on software and system development. In this paper, we present early findings of an experiment to assess the extent to which the number and type of examples used in security training stimuli can impact security problem solving. To motivate this research, we formulate hypotheses from analogical transfer theory in psychology. The independent variables include number of problem surfaces and schemas, and the dependent variable is the answer accuracy. Our study results do not show a statistically significant difference in performance when the number and types of examples are varied. We discuss the limitations, threats to validity and opportunities for future studies in this area.

The Symposium and Bootcamp on the Science of Security (HotSoS), is a research event centered on the Science of Security (SoS). Following a successful invitational SoS Community Meeting in December 2012, HotSoS 2014 was the first open research event in what we expect will be a continuing series of such events. The key motivation behind developing a Science of Security is to address the fundamental problems of cybersecurity in a principled manner. Security has been intensively studied, but a lot of previous research emphasizes the engineering of specific solutions without first developing the scientific understanding of the problem domain. All too often, security research conveys the flavor of identifying specific threats and removing them in an apparently ad hoc manner. The motivation behind the nascent Science of Security is to understand how computing systems are architected, built, used, and maintained with a view to understanding and addressing security challenges systematically across their life cycle. In particular, two features distinguish the Science of Security from previous research programs on cybersecurity. Scope. The Science of Security considers not just computational artifacts but also incorporates the human, social, and organizational aspects of computing within its purview. Approach. The Science of Security takes a decidedly scientific approach, based on the understanding of empirical evaluation and theoretical foundations as developed in the natural and social sciences, but adapted as appropriate for the "artificial science" (paraphrasing Herb Simon's term) that is computing.