A Game-Theoretic Approach for Threats Detection and Intervention in Surveillance

Threat prevention with limited security resources is a challenging problem. An optimal strategy is to eectively predict attackers' targets (or goals) based on current available information, and use such predictions to prevent (or disrupt) their planned attacks. In this paper, we propose a game-theoretic framework to address this challenge which encompasses the following three elements. First, we design a method to analyze an attacker's types in order to determine the most plausible type of an attacker. Second, we propose an approach to predict possible targets of an attack and the course of actions that the attackers may take even when the attackers' types are ambiguous. Third, a game-theoretic based strategy is developed to determine the best protection actions for defenders (security resources).

Detection Limits of Organic Compounds Achievable with Intense, Short-Pulse Lasers

Many organic molecules have strong absorption bands which can be accessed by ultraviolet short pulse lasers to produce efficient ionization. This resonant multiphoton ionization scheme has already been exploited as an ionization source in time-of-flight mass spectrometers used for environmental trace analysis. In the present work we quantify the ultimate potential of this technique by measuring absolute ion yields produced from the interaction of 267 nm femtosecond laser pulses with the organic molecules indole and toluene, and gases Xe, N2 and O2. Using multiphoton ionization cross sections extracted from these results, we show that the laser pulse parameters required for real-time detection of aromatic molecules at concentrations of one part per trillion in air and a limit of detection of a few attomoles are achievable with presently available commercial laser systems. The potential applications for the analysis of human breath, blood and tissue samples are discussed.

GALEX Detection of Shock Breakout in Type IIP Supernova PS1-13arp: Implications for the Progenitor Star Wind

We present the GALEX detection of a UV burst at the time of explosion of an optically normal supernova (SN) IIP (PS1-13arp) from the Pan-STARRS1 survey at z = 0.1665. The temperature and luminosity of the UV burst match the theoretical predictions for shock breakout in a red supergiant (RSG), but with a duration a factor of similar to 50 longer than expected. We compare the NUV light curve of PS1-13arp to previous GALEX detections of SNe IIP and find clear distinctions that indicate that the UV emission is powered by shock breakout, and not by the subsequent cooling envelope emission previously detected in these systems. We interpret the similar to 1 day duration of the UV signal with a shock breakout in the wind of an RSG with a pre-explosion mass-loss rate of similar to 10(-3) M-circle dot yr(-1). This mass-loss rate is enough to prolong the duration of the shock breakout signal, but not enough to produce an excess in the optical plateau light curve or narrow emission lines powered by circumstellar interaction. This detection of nonstandard, potentially episodic high mass loss in an RSG SN progenitor has favorable consequences for the prospects of future wide-field UV surveys to detect shock breakout directly in these systems, and provide a sensitive probe of the pre-explosion conditions of SN progenitors.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

Realist synthesis: Critical care rapid response systems.

Background Rapid Response Systems (RRS) consist of four interrelated and interdependent components; an event detection and trigger mechanism, a response strategy, a governance structure and process improvement system. These multiple components of the RRS pose problems in evaluation as the intervention is complex and cannot be evaluated using a traditional systematic review. Complex interventions in healthcare aimed at changing service delivery and related behaviour of health professionals require a different approach to summarising the evidence. Realist synthesis is such an approach to reviewing research evidence on complex interventions to provide an explanatory analysis of how and why an intervention works or doesn’t work in practice. The core principle is to make explicit the underlying assumptions about how an intervention is suppose to work (ie programme theory) and then use this theory to guide evaluation. Methods A realist synthesis process was used to explain those factors that enable or constrain the success of RRS programmes. Results The findings from the review include the articulation of the RRS programme theories, evaluation of whether these theories are supported or refuted by the research evidence and an evaluation of evidence to explain the underlying reasons why RRS works or doesn’t work in practice. Rival conjectured RRS programme theories were identified to explain the constraining factors regarding implementation of RRS in practice. These programme theories are presented using a logic model to highlight all the components which impact or influence the delivery of RRS programmes in the practice setting. The evidence from the realist synthesis provided the foundation for the development of hypothesis to test and refine the theories in the subsequent stages of the Realist Evaluation PhD study [1]. This information will be useful in providing evidence and direction for strategic and service planning of acute care to improve patient safety in hospital. References: McGaughey J, Blackwood B, O’Halloran P, Trinder T. J. & Porter S. (2010) Realistic Evaluation of Early Warning Systems and the Acute Life-threatening Events – Recognition and Treatment training course for early recognition and management of deteriorating ward-based patients: research protocol. Journal of Advanced Nursing 66 (4), 923-932.

Early warning systems: problems, pragmatics and potential

Symposium Chair: Dr Jennifer McGaughey

Title: Early Warning Systems: problems, pragmatics and potential

Early Warning Systems (EWS) provide a mechanism for staff to recognise, refer and manage deteriorating patients on general hospital wards. Implementation of EWS in practice has required considerable change in the delivery of critical care across hospitals. Drawing their experience of these changes the authors will demonstrate the problems and potential of using EWS to improve patient outcomes.

The first paper (Dr Jennifer McGaughey: Early Warning Systems: what works?) reviews the research evidence regarding the factors that support or constrain the implementation of Early Warning System (EWS) in practice. These findings explain those processes which impact on the successful achievement of patient outcomes. In order to improve detection and standardise practice National EWS have been implemented in the United Kingdom. The second paper (Catherine Plowright: The implementation of the National EWS in a District General Hospital) focuses on the process of implementing and auditing a National EWS. This process improvement is essential to contribute to future collaborative research and collection of robust datasets to improve patient safety as recommended by the Royal College of Physicians (RCP 2012). To successfully implement NEWS in practice requires strategic planning and staff education. The practical issues of training staff is discussed in the third paper. This paper (Collette Laws-Chapman: Simulation as a modality to embed the use of Early Warning Systems) focuses on using simulation and structured debrief to enhance learning in the early recognition and management of deteriorating patients. This session emphasises the importance of cognitive and social skills developed alongside practical skills in the simulated setting.

Star-planet interactions and selection effects from planet detection methods

Planets may have effects on their host stars by tidal or magnetic interaction. Such star-planet interactions are thought to enhance the activity level of the host star. However, stellar activity also affects the sensitivity of planet detection methods. Samples of planet-hosting stars which are investigated for such star-planet interactions are therefore subject to strong selection effects which need to be taken into account.

Development of a Novel Assay for the Detection of Active Neutrophil Elastase in Patients with Chronic Obstructive Pulmonary Disease

Neutrophil elastase (NE), a biomarker of infection and inflammation, correlates with the severity of several respiratory diseases including chronic obstructive pulmonary disease (COPD). However, it’s detection and quantification in biological samples is confounded by a lack of reliable and robust methodologies. Standard assays using chromogenic or fluorogenic substrates are not specific when added to complex clinical samples containing multiple proteolytic and hydrolytic enzymes which have the ability to hydrolyse the substrate, thereby resulting in an over-estimation of the target protease. Furthermore, ELISA systems measure total protease levels which can be a mixture of latent, active and protease-inhibitor complexes. Therefore, we have developed a novel immunoassay (ProteaseTag™ Active NE Immunoassay) which is selective and specific for the capture of active NE in sputum and Bronchoalveolar Lavage (BAL) in patients with COPD. The objective of this study was to clinically validate ProteaseTag™ Active NE Ultra Immunoassay for the detection of NE in sputum from COPD patients. 20 matched sputum sol samples were collected from 10 COPD patients (M=6, F=4; 73 ± 6 years) during stable and exacerbation phases. Samples were assayed for NE activity utilising both ProteaseTag™ Active NE Ultra Immunoassay and a fluorogenic substrate-based kinetic activity assay. Both assays detected elevated levels of NE in the majority of patients (n=7) during an exacerbation (mean=217.2 μg/ml ±296.6) compared to their stable phase (mean=92.37 μg/ml ±259.8). However, statistical analysis did not show this difference to be significant (p=0.07, ProteaseTag™ Active NE Ultra Immunoassay; p=0.06 kinetic assay), most likely due to the low study number. A highly significant correlation was found between the 2 assay types (p≤0.0001, r=0.996). NE as a primary efficacy endpoint in clinical trials or as a marker of inflammation within the clinic has been hampered by the lack of a robust and simple to use assay. ProteaseTag™ Active NE Immunoassay specifically measures only active NE in clinical samples, is quick and easy to use (< 3 hours) and has no dependency on a kinetic readout. ProteaseTag™ technology is currently being transferred to a lateral flow device for use at Point of Care.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

A validated UPLC–MS/MS method for the surveillance of ten aquatic biotoxins in European brackish and freshwater systems

Over the past few decades, there has been an increased frequency and duration of cyanobacterial Harmful Algal Blooms (HABs) in freshwater systems globally. These can produce secondary metabolites called cyanotoxins, many of which are hepatotoxins, raising concerns about repeated exposure through ingestion of contaminated drinking water or food or through recreational activities such as bathing/ swimming. An ultra-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS) multi-toxin method has been developed and validated for freshwater cyanotoxins; microcystins-LR, -YR, -RR, -LA, -LY and -LF, nodularin, cylindrospermopsin, anatoxin-a and the marine diatom toxin domoic acid. Separation was achieved in around 9 min and dual SPE was incorporated providing detection limits of between 0.3 and 5.6 ng/L of original sample. Intra- and inter-day precision analysis showed relative
standard deviations (RSD) of 1.2–9.6% and 1.3–12.0% respectively. The method was applied to the analysis of aquatic samples (n = 206) from six European countries. The main class detected were the hepatotoxins; microcystin-YR (n = 22), cylindrospermopsin (n = 25), microcystin-RR (n = 17), microcystin-LR (n = 12), microcystin-LY (n = 1), microcystin-LF (n = 1) and nodularin (n = 5). For microcystins, the levels detected ranged from 0.001 to 1.51 mg/L, with two samples showing combined levels above the guideline set by the WHO of 1 mg/L for microcystin-LR. Several samples presented with multiple toxins indicating the potential for synergistic effects and possibly enhanced toxicity. This is the first published pan European survey of freshwater bodies for multiple biotoxins, including two identified for the first time; cylindrospermopsin in Ireland and nodularin in Germany, presenting further incentives for improved monitoring and development of strategies to mitigate human exposure.

Threshold Optimization for Energy Detection-Based Spectrum Sensing Over Hyper-Rayleigh Fading Channels

Spectrum sensing is a key function of cognitive radio systems. Sensing performance is determined by three main factors including the wireless channel between the primary system and the cognitive radio nodes, the detection threshold, and the sensing time. In this letter a closed-form expression for the average probability of detection for energy detection based spectrum sensing over two-wave with diffuse power fading channels is derived. This expression is then used to optimize the detection threshold for cognitive radio nodes, which operate in confined structures that exhibit worse than Rayleigh fading conditions. Such fading conditions can represent a behavioral model of cognitive machine-to-machine systems deployed in enclosed structures such as in-vehicular environments.

Energy Detection based Spectrum Sensing over Two-wave with Diffuse Power Fading Channels

One of the most important factors that affects the performance of energy detection (ED) is the fading channel between the wireless nodes. This article investigates the performance of ED-based spectrum sensing, for cognitive radio (CR), over two-wave with diffuse power (TWDP) fading channels. The TWDP fading model characterizes a variety of fading channels, including well-known canonical fading distributions, such as Rayleigh and Rician, as well as worse than Rayleigh fading conditions modeled by the two-ray fading model. Novel analytic expressions for the average probability of detection over TWDP fading that account for single-user and cooperative spectrum sensing as well as square law selection diversity reception are derived. These expressions are used to analyze the behavior of ED-based spectrum sensing over moderate, severe and extreme fading conditions, and to investigate the use of cooperation and diversity as a means of mitigating the fading effects. Our results indicate that TWDP fading conditions can significantly degrade the sensing performance; however, it is shown that detection performance can be improved when cooperation and diversity are employed. The presented outcomes enable us to identify the limits of ED-based spectrum sensing and quantify the trade-offs between detection performance and energy efficiency for cognitive radio systems deployed within confined environments such as in-vehicular wireless networks.

Real-Time Multiple Event Detection and Classification Using Moving Window PCA

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load) and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, thereby increasing overall situational awareness of the power system. Numerical case studies using both real data, collected from the UK power system, and simulated case studies, constructed using DigSilent PowerFactory, for islanding events, as well as both loss of load and generation dip events, are used to demonstrate the reliability of the proposed method.

Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis – moving out of the laboratory and into the food supply chain

Major food adulteration and contamination events occur with alarming regularity and are known to be episodic, with the question being not if but when another large-scale food safety/integrity incident will occur. Indeed, the challenges of maintaining food security are now internationally recognised. The ever increasing scale and complexity of food supply networks can lead to them becoming significantly more vulnerable to fraud and contamination, and potentially dysfunctional. This can make the task of deciding which analytical methods are more suitable to collect and analyse (bio)chemical data within complex food supply chains, at targeted points of vulnerability, that much more challenging. It is evident that those working within and associated with the food industry are seeking rapid, user-friendly methods to detect food fraud and contamination, and rapid/high-throughput screening methods for the analysis of food in general. In addition to being robust and reproducible, these methods should be portable and ideally handheld and/or remote sensor devices, that can be taken to or be positioned on/at-line at points of vulnerability along complex food supply networks and require a minimum amount of background training to acquire information rich data rapidly (ergo point-and-shoot). Here we briefly discuss a range of spectrometry and spectroscopy based approaches, many of which are commercially available, as well as other methods currently under development. We discuss a future perspective of how this range of detection methods in the growing sensor portfolio, along with developments in computational and information sciences such as predictive computing and the Internet of Things, will together form systems- and technology-based approaches that significantly reduce the areas of vulnerability to food crime within food supply chains. As food fraud is a problem of systems and therefore requires systems level solutions and thinking.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.