Detection of radioactive materials in an urban environment usually requires large, portal-monitor-style radiation
detectors. However, this may not be a practical solution in many transport scenarios. Alternatively, a distributed sensor
network (DSN) could complement portal-style detection of radiological materials through the implementation of arrays
of low cost, small heterogeneous sensors with the ability to detect the presence of radioactive materials in a moving
vehicle over a specific region. In this paper, we report on the use of a heterogeneous, wireless, distributed sensor
network for traffic monitoring in a field demonstration. Through wireless communications, the energy spectra from
different radiation detectors are combined to improve the detection confidence. In addition, the DSN exploits other
sensor technologies and algorithms to provide additional information about the vehicle, such as its speed, location, class
(e.g. car, truck), and license plate number. The sensors are in-situ and data is processed in real-time at each node.
Relevant information from each node is sent to a base station computer which is used to assess the movement of
radioactive materials.
To detect malicious executables, often spread as email attachments, two types of algorithms are usually applied under instance-based statistical learning paradigms: (1) Signature-based template matching, which finds unique tell-tale characteristics of a malicious executable and thus is capable of matching those with known signatures; (2) Two-class supervised learning, which determines a set of features that allow benign and malicious patterns to occupy a disjoint regions in a feature vector space and thus probabilistically identifies malicious executables with the similar features. Nevertheless, given the huge potential variety of malicious executables, we cannot be confident that existing training sets adequately represent the class as a whole. In this study, we
investigated the use of byte sequence frequencies to profile only benign data. The malicious executables are identified as outliers or anomalies that significantly deviate from the normal profile. A multivariate Gaussian likelihood model, fit with a Principal
Component Analysis (PCA), was compared with a one-class Support Vector Machine (SVM) model for characterizing the benign executables. We found that the Gaussian model substantially outperformed the one-class SVM in its ability to distinguish
malicious from benign files. Complementing to the capabilities in reliably detecting those malicious files with known or similar features using two aforementioned methods, the one-class unsupervised approach may provide another layer of safeguard in identifying those novel computer viruses.
We propose a novel approach for identifying the "most unusual" samples in a data set, based on a resampling of data attributes. The
resampling produces a "background class" and then binary
classification is used to distinguish the original training set from
the background. Those in the training set that are most like the
background (i.e., most unlike the rest of the training set) are considered anomalous. Although by their nature, anomalies do not
permit a positive definition (if I knew what they were, I wouldn't
call them anomalies), one can make "negative definitions" (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures.
We investigate the dynamics of a pair of electrically coupled pacemaking sino-atrial node cells based on a physiologically detailed model. Each cell has distinguished oscillation properties. It is found that at low, yet still physiologically reasonable coupling conductance values, complex dynamics including chaos can arise. Occurrence of these complex dynamics in coupled pacemaker cells may provide an explanation for the origin of certain cardiac arrhythmias.
Conference Committee Involvement (2)
Cyber Security, Situation Management, and Impact Assessment II
8 April 2010 | Orlando, Florida, United States
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006
17 April 2006 | Orlando (Kissimmee), Florida, United States
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