Biblio

Healthcare professionals have unique motivations, goals, perceptions, training, tensions, and behaviors, which guide workflow and often lead to unprecedented workarounds that weaken the efficacy of security policies and mechanisms. Identifying and understanding these factors that contribute to circumvention, as well as the acts of circumvention themselves, is key to designing, implementing, and maintaining security subsystems that achieve security goals in healthcare settings. To this end, we present our research on workarounds to computer security in healthcare settings without compromising the fundamental health goals. We argue and demonstrate that understanding workarounds to computer security, especially in medical settings, requires not only analyses of computer rules and processes, but also interviews and observations with users and security personnel. In addition, we discuss the value of shadowing clinicians and conducting focus groups with them to understand their motivations and tradeoffs for circumvention. Ethnographic investigation of workflow is paramount to achieving security objectives.

This paper presents a factor graph based framework (namely AttackTagger)
for high accuracy and preemptive detection of attacks. We use security logs
on real-incidents that occurred over a six-year period at the National Cen-
ter for Supercomputing Applications (NCSA) at the University of Illinois at
Urbana-Champaign to evaluate AttackTagger. Our data consist of attacks
that led directly to the target system being compromised, i.e., not detected
in advance, either by the security analysts or by intrusion detection sys-
tems. AttackTagger detected 74 percent of attacks, a vast majority of them
were detected before the system misuse. AttackTagger uncovered six hidden
attacks that were not detected by security analysts.

This paper presents a factor graph based framework (namely AttackTagger) for high accuracy and preemptive detection of attacks. We use security logs on real-incidents that occurred over a six-year period at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign to evaluate AttackTagger. Our data consist of attacks that led directly to the target system being compromised, i.e., not detected in advance, either by the security analysts or by intrusion detection systems. AttackTagger detected 74 percent of attacks, a vast majority of them were detected before the system misuse. AttackTagger uncovered six hidden attacks that were not detected by security analysts.

We study the Lp induced gain of discretetime linear switching systems with graph-constrained switching sequences. We first prove that, for stable systems in a minimal realization, for every p ≥ 1, the Lp-gain is exactly characterized through switching storage functions. These functions are shown to be the pth power of a norm. In order to consider general systems, we provide an algorithm for computing minimal realizations. These realizations are rectangular systems, with a state dimension that varies according to the mode of the system. We apply our tools to the study on the of L2-gain. We provide algorithms for its approximation, and provide a converse result for the existence of quadratic switching storage functions. We finally illustrate the results with a physically motivated example.

Consider a thin, flexible wire of fixed length that is held at each end by a robotic gripper. The curve traced by this wire can be described as a local solution to a geometric optimal control problem, with boundary conditions that vary with the position and orientation of each gripper. The set of all local solutions to this problem is the configuration space of the wire under quasi-static manipulation. We will show that this configuration space is a smooth manifold of finite dimension that can be parameterized by a single chart. Working in this chart—rather than in the space of boundary conditions—makes the problem of manipulation planning very easy to solve. Examples in simulation illustrate our approach.

In this paper, we introduce and experimentally validate a sampling-based planning algorithm for quasi-static manipulation of a planar elastic rod. Our algorithm is an immediate consequence of deriving a global coordinate chart of finite dimension that suffices to describe all possible configurations of the rod that can be placed in static equilibrium by fixing the position and orientation of each end. Hardware experiments confirm this derivation in the case where the “rod” is a thin, flexible strip of metal that has a fixed base and that is held at the other end by an industrial robot. We show an example in which a path of the robot that was planned by our algorithm causes the metal strip to move between given start and goal configurations while remaining in quasi-static equilibrium.

In this work we explore how different cognitive processes affected typing patterns through a computer game we call The Typing Game. By manipulating the players' familiarity with the words in our game through their similarity to dictionary words, and by allowing some players to replay rounds, we found that typing speed improves with familiarity with words, and also with practice, but that these are independent of the number of mistakes that are made when typing. We also found that users who had the opportunity to replay rounds exhibited different typing patterns even before replaying the rounds.

In this work we explore how different cognitive processes affected typing patterns through a computer game we call The Typing Game. By manipulating the players' familiarity with the words in our game through their similarity to dictionary words, and by allowing some players to replay rounds, we found that typing speed improves with familiarity with words, and also with practice, but that these are independent of the number of mistakes that are made when typing. We also found that users who had the opportunity to replay rounds exhibited different typing patterns even before replaying the rounds.

In this work we explore how different cognitive processes af- fected typing patterns through a computer game we call The Typing Game. By manipulating the players’ familiarity with the words in our game through their similarity to dictionary words, and by allowing some players to replay rounds, we found that typing speed improves with familiarity with words, and also with practice, but that these are independent of the number of mistakes that are made when typing. We also found that users who had the opportunity to replay rounds exhibited different typing patterns even before replaying the rounds. 

If you’re involved in cybersecurity as a software developer, forensic investigator, or network administrator, this practical guide shows you how to apply the scientific method when assessing techniques for protecting your information systems. You’ll learn how to conduct scientific experiments on everyday tools and procedures, whether you’re evaluating corporate security systems, testing your own security product, or looking for bugs in a mobile game.

Once author Josiah Dykstra gets you up to speed on the scientific method, he helps you focus on standalone, domain-specific topics, such as cryptography, malware analysis, and system security engineering. The latter chapters include practical case studies that demonstrate how to use available tools to conduct domain-specific scientific experiments.

• Learn the steps necessary to conduct scientific experiments in cybersecurity
• Explore fuzzing to test how your software handles various inputs
• Measure the performance of the Snort intrusion detection system
• Locate malicious “needles in a haystack” in your network and IT environment
• Evaluate cryptography design and application in IoT products
• Conduct an experiment to identify relationships between similar malware binaries
• Understand system-level security requirements for enterprise networks and web services

Hadoop has become increasingly popular as it rapidly processes data in parallel. Cloud computing gives reliability, flexibility, scalability, elasticity and cost saving to cloud users. Deploying Hadoop in cloud can benefit Hadoop users. Our evaluation exhibits that various internal cloud attacks can bypass current Hadoop security mechanisms, and compromised Hadoop components can be used to threaten overall Hadoop. It is urgent to improve compromise resilience, Hadoop can maintain a relative high security level when parts of Hadoop are compromised. Hadoop has two vulnerabilities that can dramatically impact its compromise resilience. The vulnerabilities are the overloaded authentication key, and the lack of fine-grained access control at the data access level. We developed a security enhancement for a public cloud-based Hadoop, named SEHadoop, to improve the compromise resilience through enhancing isolation among Hadoop components and enforcing least access privilege for Hadoop processes. We have implemented the SEHadoop model, and demonstrated that SEHadoop fixes the above vulnerabilities with minimal or no run-time overhead, and effectively resists related attacks.

To help establish a more scientific basis for security science, which will enable the development of fundamental theories and move the field from being primarily reactive to primarily proactive, it is important for research results to be reported in a scientifically rigorous manner. Such reporting will allow for the standard pillars of science, namely replication, meta-analysis, and theory building. In this paper we aim to establish a baseline of the state of scientific work in security through the analysis of indicators of scientific research as reported in the papers from the 2015 IEEE Symposium on Security and Privacy. To conduct this analysis, we developed a series of rubrics to determine the completeness of the papers relative to the type of evaluation used (e.g. case study, experiment, proof). Our findings showed that while papers are generally easy to read, they often do not explicitly document some key information like the research objectives, the process for choosing the cases to include in the studies, and the threats to validity. We hope that this initial analysis will serve as a baseline against which we can measure the advancement of the science of security.

As smart meters continue to be deployed around the world collecting unprecedented levels of fine-grained data about consumers, we need to find mechanisms that are fair to both, (1) the electric utility who needs the data to improve their operations, and (2) the consumer who has a valuation of privacy but at the same time benefits from sharing consumption data. In this paper we address this problem by proposing privacy contracts between electric utilities and consumers with the goal of maximizing the social welfare of both. Our mathematical model designs an optimization problem between a population of users that have different valuations on privacy and the costs of operation by the utility. We then show how contracts can change depending on the probability of a privacy breach. This line of research can help inform not only current but also future smart meter collection practices.

The objective of this work is to develop an efficient and practical sensor placement method for the failure detection and localization in water networks. We formulate the problem as the minimum test cover problem (MTC) with the objective of selecting the minimum number of sensors required to uniquely identify and localize pipe failure events. First, we summarize a single-level sensing model and discuss an efficient fast greedy approach for solving the MTC problem. Simulation results on benchmark test networks demonstrate the efficacy of the fast greedy algorithm. Second, we develop a multi-level sensing model that captures additional physical features of the disturbance event, such as the time lapsed between the occurrence of disturbance and its detection by the sensor. Our sensor placement approach using MTC extends to the multi-level sensing model and an improved identification performance is obtained via reduced number of sensors (in comparison to single-level sensing model). In particular, we investigate the bi-level sensing model to illustrate the efficacy of employing multi-level sensors for the identification of failure events. Finally, we suggest extensions of our approach for the deployment of heterogeneous sensors in water networks by exploring the trade-off between cost and performance (measured in terms of the identification score of pipe/link failures).

Smart home automation and IoT promise to bring many advantages but they also expose their users to certain security and privacy vulnerabilities. For example, leaking the information about the absence of a person from home or the medicine somebody is taking may have serious security and privacy consequences for home users and potential legal implications for providers of home automation and IoT platforms. We envision that a new ecosystem within an existing smartphone ecosystem will be a suitable platform for distribution of apps for smart home and IoT devices. Android is increasingly becoming a popular platform for smart home and IoT devices and applications. Built-in security mechanisms in ecosystems such as Android have limitations that can be exploited by malicious apps to leak users’ sensitive data to unintended recipients. For instance, Android enforces that an app requires the Internet permissions in order to access a web server but it does not control which servers the app talks to or what data it shares with other apps. Therefore, sub-ecosystems that enforce additional fine-grained custom policies on top of existing policies of the smartphone ecosystems are necessary for smart home or IoT platforms. To this end, we have built a tool that enforces additional policies on inter-app interactions and permissions of Android apps. We have done preliminary testing of our tool on three proprietary apps developed by a future provider of a home automation platform. Our initial evaluation demonstrates that it is possible to develop mechanisms that allow definition and enforcement of custom security policies appropriate for ecosystems of the like smart home automation and IoT.

In this lecture, I will focus on an alternate method for addressing the problem of large state spaces. For many purposes, it may not be necessary to consider the global state as a cross-product of the states of individual actors. We take our inspiration from statistical physics where macro properties of a system may be related to the properties of individual molecules using probability distributions on the states of the latter. Consider a simple example. Suppose associated with each state is the amount of energy a node consumes when in that state (such an associated value mapping is called the reward function of the state). Now, if we have a frequency count of the nodes in each state, we can estimate the total energy consumed by the system. This suggests a model where the global state is a vector of probability mass functions (pmfs). In the above example, the size of the vector would be 5, one element for each possible state of a node. Each element of the vector represents the probability that any node is in the particular state corresponding to entry.

The concept of differential  privacy stems from the study of private query of datasets.  In  this work, we apply this concept  to metric spaces  to study a  mechanism  that randomizes a deterministic query by adding  mean-zero  noise to keep differential  privacy.

Stackelberg game models of security have received much attention, with a number of approaches for
computing Stackelberg equilibria in games with a single defender protecting a collection of targets. In contrast, multi-defender security games have received significantly less attention, particularly when each defender protects more than a single target. We fill this gap by considering a multi-defender security game, with a focus on theoretical characterizations of equilibria and the price of anarchy. We present the analysis of three models of increasing generality, two in which each defender protects multiple targets. In all models, we find that the defenders often have the incentive to over protect the targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Surprisingly, when we consider a more general model, this results obtains only in a “corner” case in the space of parameters; in most cases, however, the price of anarchy converges to a constant when the number of defenders increases.

It is critical to ensure that network policy remains consistent during state transitions. However, existing techniques impose a high cost in update delay, and/or FIB space. We propose the Customizable Consistency Generator (CCG), a fast and generic framework to support customizable consistency policies during network updates. CCG effectively reduces the task of synthesizing an update plan under the constraint of a given consistency policy to a verification problem, by checking whether an update can safely be installed in the network at a particular time, and greedily processing network state transitions to heuristically minimize transition delay. We show a large class of consistency policies are guaranteed by this greedy heuristic alone; in addition, CCG makes judicious use of existing heavier-weight network update mechanisms to provide guarantees when necessary. As such, CCG nearly achieves the “best of both worlds”: the efficiency of simply passing through updates in most cases, with the consistency guarantees of more heavyweight techniques. Mininet and physical testbed evaluations demonstrate CCG’s capability to achieve various types of consistency, such as path and bandwidth properties, with zero switch memory overhead and up to a 3× delay reduction compared to previous solutions.

We have begun to investigate the effectiveness of a phishing warning Chrome extension in a field setting of everyday computer use. A preliminary experiment has been conducted in which participants installed and used the extension. They were required to fill out an online browsing behavior questionnaire by clicking on a survey link sent in a weekly email by us. Two phishing attacks were simulated during the study by directing participants to "fake" (phishing) survey sites we created. Almost all participants who saw the warnings on our fake sites input incorrect passwords, but follow-up interviews revealed that only one participant did so intentionally. A follow-up interview revealed that the warning failure was mainly due to the survey task being mandatory. Another finding of interest from the interview was that about 50% of the participants had never heard of phishing or did not understand its meaning.

We proposed a multi-granularity approach to present risk information of mobile apps to the end users. Within this approach the highest level is a summary risk index, which allows quick and easy comparison among multiple apps that provide similar functionality. We have developed several types of risk index, such as text saying “High Risk” or number of filled circles (Gates, Chen, Li, & Proctor, 2014). Through both online and in-lab studies, we found that when presented the interface with the summary risk index, participants made more secure app-selection decisions. Subsequent research showed that framing of the summary risk information affects users’ app-selection decisions, and positive framing in terms of safety has an advantage over negative framing in terms of risk (Chen, Gates, Li, & Proctor, 2014).

In addition to the summary risk index, some users may also want more detailed risk information for the apps. We have been developing an intermediate-level risk display that presents only the major risk categories. As a first step, we conducted user studies to have expert users’ identify the major risk categories (personal privacy, monetary loss, and device stability) and validate the categories on typical users (Jorgensen, Chen, Gates, Li, Proctor, & Yu, 2015). In a subsequent study, we are developing a graphical display to incorporate these risk categories into the current app interface and test its effectiveness.

This multi-granularity approach can be applied to risk communication in other contexts. For example, in the context of communicating the potential risk associated with phishing attacks, an effective warning should be designed to include both higher-level and lower-level risk information: A higher-level index information about how likely an email message or website is a phishing one should be presented to users and inform them about the potential risk in an easy-to-comprehend manner; a more detailed explanation should also be available for users who want to know more about the warning and the index. We have completed a pilot study in this area and are initiating a full study to investigate the effectiveness of such an interface in preventing users from being phished successfully.

This article presents a systematic review on the challenges and recent progress of timing and carrier synchronization techniques for high-speed optical transmission systems using single-carrier-based coherent optical modulation formats.