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.

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).

Stackelberg security games have been widely deployed in recent years to schedule security resources. An assumption in most existing security game models is that one security resource assigned to a target only protects that target. However, in many important real-world security scenarios, when a resource is assigned to a target, it exhibits protection externalities: that is, it also protects other "neighbouring" targets. We investigate such Security Games with Protection Externalities (SPEs). First, we demonstrate that computing a strong Stackelberg equilibrium for an SPE is NP-hard, in contrast with traditional Stackelberg security games which can be solved in polynomial time. On the positive side, we propose a novel column generation based approach—CLASPE—to solve SPEs. CLASPE features the following novelties: 1) a novel mixed-integer linear programming formulation for the slave problem; 2) an extended greedy approach with a constant-factor approximation ratio to speed up the slave problem; and 3) a linear-scale linear programming that efficiently calculates the upper bounds of target-defined subproblems for pruning. Our experimental evaluation demonstrates that CLASPE enable us to scale to realistic-sized SPE problem instances.

This paper introduces a novel team-triggered algorithmic solution for a distributed optimal deployment problem involving a group of mobile sensors. Distributed self-triggered algorithms relieve the requirement of synchronous periodic communication among agents by providing opportunistic criteria for when communication should occur. However, these criteria are often conservative since worst-case scenarios must always be considered to ensure the monotonic evolution of a relevant objective function. Here we introduce a team-triggered algorithm that builds on the idea of `promises' among agents, allowing them to operate with better information about their neighbors when they are not communicating, over a dynamically changing graph. We analyze the correctness of the proposed strategy and establish the same convergence guarantees as a coordination algorithm that assumes perfect information at all times. The technical approach relies on tools from set-valued stability analysis, computational geometry, and event-based systems. Simulations illustrate our results.

Because of poor performance of heuristic algorithms on virtual machine placement problem in cloud environments, a multi-objective constraint optimization model of virtual machine placement is presented, which taking energy consumption and resource wastage as the objective. We solve the model based on the proposed discrete firefly algorithm. It takes firefly's location as the placement result, brightness as the objective value. Its movement strategy makes darker fireflies move to brighter fireflies in solution space. The continuous position after movement is discretized by the proposed discrete strategy. In order to speed up the search for solution, the local search mechanism for the optimal solution is introduced. The experimental results in OpenStack cloud platform show that the proposed algorithm makes less energy consumption and resource wastage compared with other algorithms.

In today’s inter-connected world, threats from anywhere in the world can have serious global repercussions. In particular, two types of threats have a global impact: 1) cyber crime and 2) cyber and biological weapons. If a country’s environment is conducive to cyber criminal activities, cyber criminals will use that country as a basis to attack end-users around the world. Cyber weapons and biological weapons can now allow a small actor to inflict major damage on a major military power. If cyber and biological weapons are used in combination, the damage can be amplified significantly. Given that the cyber and biological threat is global, it is important to identify countries that pose the greatest threat and design action plans to reduce the threat from these countries. However, prior work on cyber crime lacks empirical substantiation for reasons why some countries’ environments are conducive to cyber crime. Prior work on cyber and biological weapon capabilities mainly consists of case studies which only focus on select countries and thus are not generalizeable. To sum up, assessing the global cyber and biological threat currently lacks a systematic empirical approach. In this thesis, I take an empirical and systematic approach towards assessing the global cyber and biological threat. The first part of the thesis focuses on cyber crime. I examine international variation in cyber crime infrastructure hosting and cyber crime exposure. I also empirically test hypotheses about factors behind such variation. In that work, I use Symantec’s telemetry data, collected from 10 million Symantec customer computers worldwide and accessed through the Symantec’s Worldwide Intelligence Network Environment (WINE). I find that addressing corruption in Eastern Europe or computer piracy in Sub-Saharan Africa has the potential to reduce the global cyber crime. The second part of the thesis focuses on cyber and biological weapon capabilities. I develop two computational methodologies: one to assess countries’ biological capabilities and one to assess countries’ cyber capabilities. The methodologies examine all countries in the world and can be used by non-experts that only have access to publicly available data. I validate the biological weapon assessment methodology by comparing the methodology’s assessment to historical data. This work has the potential to proactively reduce the global cyber and biological weapon threat.

Security has consistently been the focus of attention in many highly-configurable software systems. Several vulnerabilities on widely-used systems, such as the Linux kernel and OpenSSL, are reported every day in the National Vulnerability Database (NVD). The configurability of these systems enables the rapid generation of customized products, but also creates security challenges in the development and maintenance processes. For instance, interactions caused by configurations may create serious security threats and make generated products more susceptible to attacks [6], but the causes of these problems may be harder to detect because they occur only in specific configurations.

The number of trojans, worms, and viruses that computers encounter varies greatly across countries. Empirically identifying factors behind such variation can provide a scientific empirical basis to policy actions to reduce malware encounters in the most affected countries. However, our understanding of these factors is currently mainly based on expert opinions, not empirical evidence.

In this paper, we empirically test alternative hypotheses about factors behind international variation in the number of trojan, worm, and virus encounters. We use the Symantec Anti-Virus (AV) telemetry data collected from more than 10 million Symantec customer computers worldwide that we accessed through the Symantec Worldwide Intelligence Environment (WINE) platform. We use regression analysis to test for the effect of computing and monetary resources, web browsing behavior, computer piracy, cyber security expertise, and international relations on international variation in malware encounters.

We find that trojans, worms, and viruses are most prevalent in Sub-Saharan African countries. Many Asian countries also encounter substantial quantities of malware. Our regression analysis reveals that the main factor that explains high malware exposure of these countries is a widespread computer piracy especially when combined with poverty. Our regression analysis also reveals that, surprisingly, web browsing behavior, cyber security expertise, and international relations have no significant effect.

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.

Cyber-Physical Systems (CPS) are systems with seamless integration of physical, computational and networking components. These systems can potentially have an impact on the physical components, hence it is critical to safeguard them against a wide range of attacks. In this paper, it is argued that an effective approach to achieve this goal is to systematically identify the potential threats at the design phase of building such systems, commonly achieved via threat modeling. In this context, a tool to perform systematic analysis of threat modeling for CPS is proposed. A real-world wireless railway temperature monitoring system is used as a case study to validate the proposed approach. The threats identified in the system are subsequently mitigated using National Institute of Standards and Technology (NIST) standards.

Self-adaptive systems tend to be reactive and myopic, adapting in response to changes without anticipating what the subsequent adaptation needs will be. Adapting reactively can result in inefficiencies due to the system performing a suboptimal sequence of adaptations. Furthermore, when adaptations have latency, and take some time to produce their effect, they have to be started with sufficient lead time so that they complete by the time their effect is needed. Proactive latency-aware adaptation addresses these issues by making adaptation decisions with a look-ahead horizon and taking adaptation latency into account. In this paper we present an approach for proactive latency-aware adaptation under uncertainty that uses probabilistic model checking for adaptation decisions. The key idea is to use a formal model of the adaptive system in which the adaptation decision is left underspecified through nondeterminism, and have the model checker resolve the nondeterministic choices so that the accumulated utility over the horizon is maximized. The adaptation decision is optimal over the horizon, and takes into account the inherent uncertainty of the environment predictions needed for looking ahead. Our results show that the decision based on a look-ahead horizon, and the factoring of both tactic latency and environment uncertainty, considerably improve the effectiveness of adaptation decisions.

Cyber-Physical Systems (CPS) are systems with seamless integration of physical, computational and networking components. These systems can potentially have an impact on the physical components, hence it is critical to safeguard them against a wide range of attacks. In this paper, it is argued that an effective approach to achieve this goal is to systematically identify the potential threats at the design phase of building such systems, commonly achieved via threat modeling. In this context, a tool to perform systematic analysis of threat modeling for CPS is proposed. A real-world wireless railway temperature monitoring system is used as a case study to validate the proposed approach. The threats identified in the system are subsequently mitigated using National Institute of Standards and Technology (NIST) standards.

In this work we are interested in the stability and L2-gain of hybrid systems with linear flow dynamics, periodic time-triggered jumps and nonlinear possibly set-valued jump maps. This class of hybrid systems includes various interesting applications such as periodic event-triggered control. In this paper we also show that sampled-data systems with arbitrarily switching controllers can be captured in this framework by requiring the jump map to be set-valued. We provide novel conditions for the internal stability and L2-gain analysis of these systems adopting a lifting-based approach. In particular, we establish that the internal stability and contractivity in terms of an L2-gain smaller than 1 are equivalent to the internal stability and contractivity of a particular discretetime set-valued nonlinear system. Despite earlier works in this direction, these novel characterisations are the first necessary and sufficient conditions for the stability and the contractivity of this class of hybrid systems. The results are illustrated through multiple new examples.