Biblio

The split-cycle constant-period frequency modulation for flapping wing micro air vehicle control in two degrees of freedom has been proposed and its theoretical viability has been demonstrated in previous work. Further consecutive work on developing the split-cycle based physical control system has been targeted towards providing on-the-fly configurability of all the theoretically possible split-cycle wing control parameters with high fidelity on a physical Flapping Wing Micro Air Vehicle (FWMAV). Extending the physical vehicle and wing-level control modules developed previously, this paper provides the details of the FWMAV platform, that has been designed and assembled to aid other researchers interested in the design, development and analysis of high level flapping flight controllers. Additionally, besides the physical vehicle and the configurable control module, the platform provides numerous external communication access capabilities to conduct and validate various sensor fusion study for flapping flight control.

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.

In this paper, we investigate the resilient cumulant game control problem for a cyber-physical system. The cyberphysical system is modeled as a linear hybrid stochastic system with full-state feedback. We are interested in 2-player cumulant Nash game for a linear Markovian system with quadratic cost function where the players optimize their system performance by shaping the distribution of their cost function through cost cumulants. The controllers are optimally resilient against control feedback gain variations.We formulate and solve the coupled first and second cumulant Hamilton-Jacobi-Bellman (HJB) equations for the dynamic game. In addition, we derive the optimal players strategy for the second cost cumulant function. The efficiency of our proposed method is demonstrated by solving a numerical example.

Due to their low deployment costs, wireless sensor networks (WSN) may act as a key enabling technology for a variety of spatially-distributed cyber-physical system (CPS) applications, ranging from intelligent traffic control to smart grids. However, besides providing tremendous benefits in terms of deployment costs, they also open up new possibilities for malicious attackers, who aim to cause financial losses or physical damage. Since perfectly securing these spatially-distributed systems is either impossible or financially unattainable, we need to design them to be resilient to attacks: even if some parts of the system are compromised or unavailable due to the actions of an attacker, the system as a whole must continue to operate with minimal losses. In a CPS, control decisions affecting the physical process depend on the observed data from the sensor network. Any malicious activity in the sensor network can therefore severely impact the physical process, and consequently the overall CPS operations. These factors necessitate a deeper probe into the domain of resilient WSN for CPS. In this chapter, we provide an overview of various dimensions in this field, including objectives of WSN in CPS, attack scenarios and vulnerabilities, notion of attack-resilience in WSN for CPS, and solution approaches towards attaining resilience. We also highlight major challenges, recent developments, and future directions in this area.

The integration of physical systems with distributed embedded computing and communication devices offers advantages on reliability, efficiency, and maintenance. At the same time, these embedded computers are susceptible to cyber-attacks that can harm the performance of the physical system, or even drive the system to an unsafe state; therefore, it is necessary to deploy security mechanisms that are able to automatically detect, isolate, and respond to potential attacks. Detection and isolation mechanisms have been widely studied for different types of attacks; however, automatic response to attacks has attracted considerably less attention. Our goal in this paper is to identify trends and recent results on how to respond and reconfigure a system under attack, and to identify limitations and open problems. We have found two main types of attack protection: i) preventive, which identifies the vulnerabilities in a control system and then increases its resiliency by modifying either control parameters or the redundancy of devices; ii) reactive, which responds as soon as the attack is detected (e.g., modifying the non-compromised controller actions).

This article proposes Probabilistic Replacement Policy (PRP), a novel replacement policy that evicts the line with minimum estimated hit probability under optimal replacement instead of the line with maximum expected reuse distance. The latter is optimal under the independent reference model of programs, which does not hold for last-level caches (LLC). PRP requires 7% and 2% metadata overheads in the cache and DRAM respectively. Using a sampling scheme makes DRAM overhead negligible, with minimal performance impact. Including detailed overhead modeling and equal cache areas, PRP outperforms SHiP, a state-of-the-art LLC replacement algorithm, by 4% for memory-intensive SPEC-CPU2006 benchmarks.

The development of internet comes with the other domain that is cyber-crime. The record and intelligently can be exposed to a user of illegal activity so that it has become important to make the technology reliable. Phishing techniques include domain of email messages. Phishing emails have hosted such a phishing website, where a click on the URL or the malware code as executing some actions to perform is socially engineered messages. Lexically analyzing the URLs can enhance the performance and help to differentiate between the original email and the phishing URL. As assessed in this study, in addition to textual analysis of phishing URL, email classification is successful and results in a highly precise anti phishing.

Social media data and other web-based network data are large and dynamic rendering the identification of structural changes in such systems a hard problem. Typically, online data is constantly streaming and results in data that is incomplete thus necessitating the need to understand the robustness of network metrics on partial or sampled network data. In this paper, we examine the effects of sampling on key network centrality metrics using two empirical communication datasets. Correlations between network metrics of original and sampled nodes offer a measure of sampling accuracy. The relationship between sampling and accuracy is convergent and amenable to nonlinear analysis. Naturally, larger edge samples induce sampled graphs that are more representative of the original graph. However, this effect is attenuated when larger sets of nodes are recovered in the samples. Also, we find that the graph structure plays a prominent role in sampling accuracy. Centralized graphs, in which fewer nodes enjoy higher centrality scores, offer more representative samples.

Self-adaptive systems overcome many of the limitations of human supervision in complex software-intensive systems by endowing them with the ability to automatically adapt their structure and behavior in the presence of runtime changes. However, adaptation in some classes of systems (e.g., safety-critical) can benefit by receiving information from humans (e.g., acting as sophisticated sensors, decision-makers), or by involving them as system-level effectors to execute adaptations (e.g., when automation is not possible, or as a fallback mechanism). However, human participants are influenced by factors external to the system (e.g., training level, fatigue) that affect the likelihood of success when they perform a task, its duration, or even if they are willing to perform it in the first place. Without careful consideration of these factors, it is unclear how to decide when to involve humans in adaptation, and in which way. In this paper, we investigate how the explicit modeling of human participants can provide a better insight into the trade-offs of involving humans in adaptation. We contribute a formal framework to reason about human involvement in self-adaptation, focusing on the role of human participants as actors (i.e., effectors) during the execution stage of adaptation. The approach consists of: (i) a language to express adaptation models that capture factors affecting human behavior and its interactions with the system, and (ii) a formalization of these adaptation models as stochastic multiplayer games (SMGs) that can be used to analyze human-system-environment interactions. We illustrate our approach in an adaptive industrial middleware used to monitor and manage sensor networks in renewable energy production plants.

Reliability block diagram (RBD) models are a commonly used reliability analysis method. For static RBD models, combinatorial solution techniques are easy and efficient. However, static RBDs are limited in their ability to express varying system state, dependent events, and non-series-parallel topologies. A recent extension to RBDs, called Dynamic Reliability Block Diagrams (DRBD), has eliminated those limitations. This tool paper details the RBD implementation in the M¨obius modeling framework and provides technical details for using RBDs independently or in composition with other M¨obius modeling formalisms. The paper explains how the graphical front-end provides a user-friendly interface for specifying RBD models. The back-end implementation that interfaces with the M¨obius AFI to define and generate executable models that the M¨obius tool uses to evaluate system metrics is also detailed.

Reliability block diagram (RBD) models are a commonly used reliability analysis method. For static RBD models, combinatorial solution techniques are easy and efficient. However, static RBDs are limited in their ability to express varying system state, dependent events, and non-series-parallel topologies. A recent extension to RBDs, called Dynamic Reliability Block Diagrams (DRBD), has eliminated those limitations. This tool paper details the RBD implementation in the M¨obius modeling framework and provides technical details for using RBDs independently or in composition with other M¨obius modeling formalisms. The paper explains how the graphical front-end provides a user-friendly interface for specifying RBD models. The back-end implementation that interfaces with the M¨obius AFI to define and generate executable models that the M¨obius tool uses to evaluate system metrics is also detailed.

Objective: The overarching goal is to convey the concept of science of security and the contributions that a scientifically based, human factors approach can make to this interdisciplinary field.Background: Rather than a piecemeal approach to solving cybersecurity problems as they arise, the U.S. government is mounting a systematic effort to develop an approach grounded in science. Because humans play a central role in security measures, research on security-related decisions and actions grounded in principles of human information-processing and decision-making is crucial to this interdisciplinary effort.Method: We describe the science of security and the role that human factors can play in it, and use two examples of research in cybersecurity—detection of phishing attacks and selection of mobile applications—to illustrate the contribution of a scientific, human factors approach.Results: In these research areas, we show that systematic information-processing analyses of the decisions that users make and the actions they take provide a basis for integrating the human component of security science.Conclusion: Human factors specialists should utilize their foundation in the science of applied information processing and decision making to contribute to the science of cybersecurity.