Biblio

Applications in mobile marketplaces may leak private user information without notification. Existing mobile platforms provide little information on how applications use private user data, making it difficult for experts to validate appli- cations and for users to grant applications access to their private data. We propose a user-aware-privacy-control approach, which reveals how private information is used inside applications. We compute static information flows and classify them as safe/un- safe based on a tamper analysis that tracks whether private data is obscured before escaping through output channels. This flow information enables platforms to provide default settings that expose private data for only safe flows, thereby preserving privacy and minimizing decisions required from users. We build our approach into TouchDe- velop, an application-creation environment that allows users to write scripts on mobile devices and install scripts published by other users. We evaluate our approach by studying 546 scripts published by 194 users, and the results show that our approach effectively reduces the need to make access-granting choices to only 10.1 % (54) of all scripts. We also conduct a user survey that involves 50 TouchDevelop users to assess the effectiveness and usability of our approach. The results show that 90 % of the users consider our approach useful in protecting their privacy, and 54 % prefer our approach over other privacy-control approaches.

Sophistication and flexibility of software development make it easy to leave security vulnerabilities in software applications for attack- ers. It is critical to educate and train software engineers to avoid in- troducing vulnerabilities in software applications in the first place such as adopting secure coding mechanisms and conducting secu- rity testing. A number of websites provide training grounds to train people’s hacking skills, which are highly related to security test- ing skills, and train people’s secure coding skills. However, there exists no interactive gaming platform for instilling gaming aspects into the education and training of secure coding. To address this issue, we propose to construct secure coding duels in Code Hunt, a high-impact serious gaming platform released by Microsoft Re- search. In Code Hunt, a coding duel consists of two code segments: a secret code segment and a player-visible code segment. To solve a coding duel, a player iteratively modifies the player-visible code segment to match the functional behaviors of the secret code seg- ment. During the duel-solving process, the player is given clues as a set of automatically generated test cases to characterize sample functional behaviors of the secret code segment. The game aspect in Code Hunt is to recognize a pattern from the test cases, and to re-engineer the player-visible code segment to exhibit the expected behaviors. Secure coding duels proposed in this work are coding duels that are carefully designed to train players’ secure coding skills, such as sufficient input validation and access control.

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.

We examine the problem of aggregating the results of multiple anti-virus (AV) vendors' detectors into a single authoritative ground-truth label for every binary. To do so, we adapt a well-known generative Bayesian model that postulates the existence of a hidden ground truth upon which the AV labels depend. We use training based on Expectation Maximization for this fully unsupervised technique. We evaluate our method using 279,327 distinct binaries from VirusTotal, each of which appeared for the rst time between January 2012 and June 2014.

Our evaluation shows that our statistical model is consistently more accurate at predicting the future-derived ground truth than all unweighted rules of the form \k out of n" AV detections. In addition, we evaluate the scenario where partial ground truth is available for model building. We train a logistic regression predictor on the partial label information. Our results show that as few as a 100 randomly selected training instances with ground truth are enough to achieve 80% true positive rate for 0.1% false positive rate. In comparison, the best unweighted threshold rule provides only 60% true positive rate at the same false positive rate.

Computing a user-task assignment for a workflow coming with probabilistic user availability provides a measure of completion rate or resiliency. To a workflow designer this indicates a risk of failure, espe- cially useful for workflows which cannot be changed due to rigid security constraints. Furthermore, resiliency can help outline a mitigation strategy which states actions that can be performed to avoid workflow failures. A workflow with choice may have many different resiliency values, one for each of its execution paths. This makes understanding failure risk and mitigation requirements much more complex. We introduce resiliency variance, a new analysis metric for workflows which indicates volatility from the resiliency average. We suggest this metric can help determine the risk taken on by implementing a given workflow with choice. For instance, high average resiliency and low variance would suggest a low risk of workflow failure.

Workflows are complex operational processes that include security constraints restricting which users can perform which tasks. An improper user-task assignment may prevent the completion of the work- flow, and deciding such an assignment at runtime is known to be complex, especially when considering user unavailability (known as the resiliency problem). Therefore, design tools are required that allow fast evaluation of workflow resiliency. In this paper, we propose a methodology for work- flow designers to assess the impact of the security policy on computing the resiliency of a workflow. Our approach relies on encoding a work- flow into the probabilistic model-checker PRISM, allowing its resiliency to be evaluated by solving a Markov Decision Process. We observe and illustrate that adding or removing some constraints has a clear impact on the resiliency computation time, and we compute the set of security constraints that can be artificially added to a security policy in order to reduce the computation time while maintaining the resiliency.

Workflows capture complex operational processes and include security constraints limiting which users can perform which tasks. An improper security policy may prevent cer- tain tasks being assigned and may force a policy violation. Deciding whether a valid user-task assignment exists for a given policy is known to be extremely complex, especially when considering user unavailability (known as the resiliency problem). Therefore tools are required that allow automatic evaluation of workflow resiliency. Modelling well defined workflows is fairly straightforward, however user availabil- ity can be modelled in multiple ways for the same workflow. Correct choice of model is a complex yet necessary concern as it has a major impact on the calculated resiliency. We de- scribe a number of user availability models and their encod- ing in the model checker PRISM, used to evaluate resiliency. We also show how model choice can affect resiliency computation in terms of its value, memory and CPU time.

The advancement of software-defined networking (SDN) technology is highly dependent on the successful transformations from in-house research ideas to real-life products. To enable such transformations, a testbed offering scalable and high fidelity networking environment for testing and evaluating new/existing designs is extremely valuable. Mininet, the most popular SDN emulator by far, is designed to achieve both accuracy and scalability by running unmodified code of network applications in lightweight Linux Containers. How- ever, Mininet cannot guarantee performance fidelity under high workloads, in particular when the number of concurrent active events is more than the number of parallel cores. In this project, we develop a lightweight virtual time system in Linux container and integrate the system with Mininet, so that all the containers have their own virtual clocks rather than using the physical system clock which reflects the se- rialized execution of multiple containers. With the notion of virtual time, all the containers perceive virtual time as if they run independently and concurrently. As a result, inter- actions between the containers and the physical system are artificially scaled, making a network appear to be ten times faster from the viewpoint of applications within the contain- ers than it actually is. We also design an adaptive virtual time scheduling subsystem in Mininet, which is responsible to balance the experiment speed and fidelity. Experimen- tal results demonstrate that embedding virtual time into Mininet significantly enhances its performance fidelity, and therefore, results in a useful platform for the SDN community to conduct scalable experiments with high fidelity.

Realistic and scalable testing systems are critical to evaluate network applications and protocols to ensure successful real system deployments. Container-based network emula- tion is attractive because of the combination of many desired features of network simulators and physical testbeds . The success of Mininet, a popular software- defined networking (SDN) emulation testbed, demonstrates the value of such approach that we can execute unmodified binary code on a large- scale emulated network with lightweight OS-level vir- tualization techniques. However, an ordinary network em- ulator uses the system clock across all the containers even if a container is not being scheduled to run. This leads to the issue of temporal fidelity, especially with high workloads. Virtual time sheds the light on the issue of preserving tem- poral fidelity for large-scale emulation. The key insight is to trade time with system resources via precisely scaling the time of interactions between containers and physical devices by a factor of n, hence, making an emulated network ap- pear to be n times faster from the viewpoints of applications in the container. In this paper, we develop a lightweight Linux-container-based virtual time system and integrate the system to Mininet for fidelity and scalability enhancement. We also design an adaptive time dilation scheduling mod- ule for balancing speed and accuracy. Experimental results demonstrate that (1) with virtual time, Mininet is able to accurately emulate a network n times larger in scale, where n is the scaling factor, with the system behaviors closely match data obtained from a physical testbed; and (2) with the adaptive time dilation scheduling, we reduce the running time by 46% with little accuracy loss. Finally, we present a case study using the virtual-time-enabled Mininet to evalu- ate the limitations of equal-cost multi-path (ECMP) routing in a data center network.

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.

Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. Meanwhile, static configurations of networks and hosts simplify this adversarial reconnaissance. In this paper, we present a novel proactive-adaptive defense technique that turns end-hosts into untraceable moving targets, and establishes dynamics into static systems by monitoring the adversarial behavior and reconfiguring the addresses of network hosts adaptively. This adaptability is achieved by discovering hazardous network ranges and addresses and evacuating network hosts from them quickly. Our approach maximizes adaptability by (1) using fast and accurate hypothesis testing for characterization of adversarial behavior, and (2) achieving a very fast IP randomization (i.e., update) rate through separating randomization from end-hosts and managing it via network appliances. The architecture and protocols of our approach can be transparently deployed on legacy networks, as well as software-defined networks. Our extensive analysis and evaluation show that by adaptive distortion of adversarial reconnaissance, our approach slows down the attack and increases its detectability, thus significantly raising the bar against stealthy scanning, major classes of evasive scanning and worm propagation, as well as targeted (hacking) attacks.
 

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.

By enabling a direct comparison of different security solutions with respect to their relative effectiveness, a network security metric may provide quantifiable evidences to assist security practitioners in securing computer networks. However, research on security metrics has been hindered by difficulties in handling zero-day attacks exploiting unknown vulnerabilities. In fact, the security risk of unknown vulnerabilities has been considered as something unmeasurable due to the less predictable nature of software flaws. This causes a major difficulty to security metrics, because a more secure configuration would be of little value if it were equally susceptible to zero-day attacks. In this paper, we propose a novel security metric, k-zero day safety, to address this issue. Instead of attempting to rank unknown vulnerabilities, our metric counts how many such vulnerabilities would be required for compromising network assets; a larger count implies more security because the likelihood of having more unknown vulnerabilities available, applicable, and exploitable all at the same time will be significantly lower. We formally define the metric, analyze the complexity of computing the metric, devise heuristic algorithms for intractable cases, and finally demonstrate through case studies that applying the metric to existing network security practices may generate actionable knowledge.

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.
 

This paper addresses the minimum transmission broadcast (MTB) problem for the many-to-all scenario in wireless multihop networks and presents a network-coding broadcast protocol with priority-based deadlock prevention. Our main contributions are as follows: First, we relate the many-to-all-with-network-coding MTB problem to a maximum out-degree problem. The solution of the latter can serve as a lower bound for the number of transmissions. Second, we propose a distributed network-coding broadcast protocol, which constructs efficient broadcast trees and dictates nodes to transmit packets in a network coding manner. Besides, we present the priority-based deadlock prevention mechanism to avoid deadlocks. Simulation results confirm that compared with existing protocols in the literature and the performance bound we present, our proposed network-coding broadcast protocol performs very well in terms of the number of transmissions.

With the rapid development of Wireless Sensor Networks (WSNs), besides the energy efficient, Quality of Service (QoS) supported and the validity of packet transmission should be considered under some circumstances. In this paper, according to summing up LEACH protocol's advantages and defects, combining with trust evaluation mechanism, energy and QoS control, a trust-based QoS routing algorithm is put forward. Firstly, energy control and coverage scale are adopted to keep load balance in the phase of cluster head selection. Secondly, trust evaluation mechanism is designed to increase the credibility of the network in the stage of node clusting. Finally, in the period of information transmission, verification and ACK mechanism also put to guarantee validity of data transmission. In this paper, it proposes the improved protocol. The improved protocol can not only prolong nodes' life expectancy, but also increase the credibility of information transmission and reduce the packet loss. Compared to typical routing algorithms in sensor networks, this new algorithm has better performance.

Support Vector Machine (SVM) as an innovative machine learning tool, based on statistical learning theory, is recently used in process fault diagnosis tasks. In the application of SVM to a fault diagnosis problem, typically a discrete decision function with discrete output values is utilized in order to solely define the label of the fault. However, for incipient faults in which fault steadily progresses over time and there is a changeover from normal operation to faulty operation, using discrete decision function does not reveal any evidence about the progress and depth of the fault. Numerous process faults, such as the reactor fouling and degradation of catalyst, progress slowly and can be categorized as incipient faults. In this work a continuous decision function is anticipated. The decision function values not only define the fault label, but also give qualitative evidence about the depth of the fault. The suggested method is applied to incipient fault diagnosis of a continuous binary mixture distillation column and the result proves the practicability of the proposed approach. In incipient fault diagnosis tasks, the proposed approach outperformed some of the conventional techniques. Moreover, the performance of the proposed approach is better than typical discrete based classification techniques employing some monitoring indexes such as the false alarm rate, detection time and diagnosis time.

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.
 

This brief proposes a framework to analyze multiple faults based on multiple fault simulation in a particle swarm optimization environment. Experimentation shows that up to ten faults can be diagnosed in a reasonable time. However, the scheme does not put any restriction on the number of simultaneous faults.

This paper proposes a novel architecture for module partitioning problems in the process of dynamic and partial reconfigurable computing in VLSI design automation. This partitioning issue is deemed as Hypergraph replica. This can be treated by a probabilistic algorithm like the Markov chain through the transition probability matrices due to non-deterministic polynomial complete problems. This proposed technique has two levels of implementation methodology. In the first level, the combination of parallel processing of design elements and efficient pipelining techniques are used. The second level is based on the genetic algorithm optimization system architecture. This proposed methodology uses the hardware/software co-design and co-verification techniques. This architecture was verified by implementation within the MOLEN reconfigurable processor and tested on a Xilinx Virtex-5 based development board. This proposed multi-objective module partitioning design was experimentally evaluated using an ISPD’98 circuit partitioning benchmark suite. The efficiency and throughput were compared with that of the hMETIS recursive bisection partitioning approach. The results indicate that the proposed method can improve throughput and efficiency up to 39 times with only a small amount of increased design space. The proposed architecture style is sketched out and concisely discussed in this manuscript, and the existing results are compared and analyzed.

Cyber-physical systems (CPS) can potentially benefit a wide array of applications and areas. Here, the authors look at some of the challenges surrounding CPS, and consider a feasible solution for creating a robust, secure, and cost-effective architecture.