Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Intrinsic mode function synchronization measures for the anticipation of seizures in epilpsy

Epileptic seizures affect as many as 50 million people and often occur without warning or apparent provocation. We explore the applicability of noise-assisted Ensemble Empirical Mode Decomposition (EEMD) for patient-specific seizure anticipation synchronization measures as applied to the EEMD intrinsic mode function (IMF) output. Intracranial EEG data were obtained from pre-surgical monitoring at the Epilepsy Center of the University Hospital of Freiburg. Data from twenty patients were analyzed. For each recorded channel, non-overlapping time windows were submitted to the EEMD algorithm, producing twelve levels of IMFs. IMF synchronization measures (mean and maximum coherence, mean and maximum cross-correlation, correlation coefficient and synchronized phase-locking value) for channel pairs were computed and smoothed with a 20-point moving average, producing IMF-x data. Statistical distributions of IMF-x synchronization data were determined for three hours of interictal training data. Three hours of interictal validation data were used to determine the smallest zero-false-positive threshold (multiples of 0.5 standard deviations of IMF-x data) for each channel pair and IMF level. These patient-, IMF level-, and channel pair-specific IMF-x thresholds were compared against periictal (60 minutes preictal with 15 minutes ictal/postictal) IMF-x data for each seizure. Our study shows that while not all channel pairs are able to detect every ictal event, low IMF levels containing frequency components greater than –,1 Hz can discriminate between interictal and periictal activities. The anticipation window for channel pairs detecting all ictal events frequently ranged from 30 to 53 minutes prior to clinical manifestation. We propose an anticipation optimality index for a joint indicator of sensitivity and earliest anticipation times useful for selection of relevant channel pairs and IMF levels. Generalization of the analyzed synchronization measures may be appropriate for some patients, while other patients may require preferential selection of these measures. For the majority of patients, the electrode pairing type holds some relevance to performance assessment values. A strong indication of IMF-level dependence of anticipation performance data was shown, suggesting seizure dynamics in the patient-specific scenario manifest within certain frequency bandwidths. The patients with a hippocampal seizure origin show better sensitivity with our algorithm than patients with neocortical seizure origin

An Empirical Analysis of Security and Privacy in Health and Medical Systems

Healthcare reform, regulation, and adoption of technology such as wearables are substantially changing both the quality of care and how we receive it. For example, health and fitness devices contain sensors that collect data, wireless interfaces to transmit data, and cloud infrastructures to aggregate, analyze, and share data. FDA-defined class III devices such as pacemakers will soon share these capabilities. While technological growth in health care is clearly beneficial, it also brings new security and privacy challenges for systems, users, and regulators. We group these concepts under health and medical systems to connect and emphasize their importance to healthcare. Challenges include how to keep user health data private, how to limit and protect access to data, and how to securely store and transmit data while maintaining interoperability with other systems. The most critical challenge unique to healthcare is how to balance security and privacy with safety and utility concerns. Specifically, a life-critical medical device must fail-open (i.e., work regardless) in the event of an active threat or attack. This dissertation examines some of these challenges and introduces new systems that not only improve security and privacy but also enhance workflow and usability. Usability is important in this context because a secure system that inhibits workflow is often improperly used or circumvented. We present this concern and our solution in its respective chapter. Each chapter of this dissertation presents a unique challenge, or unanswered question, and solution based on empirical analysis. We present a survey of related work in embedded health and medical systems. The academic and regulatory communities greatly scrutinize the security and privacy of these devices because of their primary function of providing critical care. What we find is that securing embedded health and medical systems is hard, done incorrectly, and is analogous to non-embedded health and medical systems such as hospital servers, terminals, and personally owned mobile devices. A policy called bring your own device (BYOD) allows the use and integration of mobile devices in the workplace. We perform an analysis of Apple iMessage which both implicates BYOD in healthcare and secure messaging protocols used by health and medical systems. We analyze direct memory access engines, a special-purpose piece of hardware to transfer data into and out of main memory, and show that we can chain together memory transfers to perform arbitrary computation. This result potentially affects all computing systems used for healthcare. We also examine HTML5 web workers as they provide stealthy computation and covert communication. This finding is relevant to web applications such as personal and electronic health record portals. We design and implement two novel and secure health and medical systems. One is a wearable device that addresses the problem of authenticating a user (e.g., physician) to a terminal in a usable way. The other is a light-weight and low-cost wireless device we call Beacon+. This device extends the design of Apple's iBeacon specification with unspoofable, temporal, and authenticated advertisements; of which, enables secure location sensing applications that could improve numerous healthcare processes