Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

Analysis of the potential of cancer cell lines to release tissue factor-containing microvesicles: correlation with tissue factor and PAR2 expression

BackgroundDespite the association of cancer-derived circulating tissue factor (TF)-containing microvesicles and hypercoagulable state, correlations with the incidence of thrombosis remain unclear.MethodsIn this study the upregulation of TF release upon activation of various cancer cell lines, and the correlation with TF and PAR2 expression and/or activity was examined. Microvesicle release was induced by PAR2 activation in seventeen cell lines and released microvesicle density, microvesicle-associated TF activity, and phoshpatidylserine-mediated activity were measured. The time-course for TF release was monitored over 90 min in each cell line. In addition, TF mRNA expression, cellular TF protein and cell-surface TF activities were quantified. Moreover, the relative expression of PAR2 mRNA and cellular protein were analysed. Any correlations between the above parameters were examined by determining the Pearson’s correlation coefficients.ResultsTF release as microvesicles peaked between 30–60 min post-activation in the majority of cell lines tested. The magnitude of the maximal TF release positively correlated with TF mRNA (c = 0.717; p

InterFace : A software package for face image warping, averaging, and principal components analysis

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the “face space” produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment

Purinergic signalling and immune cells

This review article provides a historical perspective on the role of purinergic signalling in the regulation of various subsets of immune cells from early discoveries to current understanding. It is now recognised that adenosine 5'-triphosphate (ATP) and other nucleotides are released from cells following stress or injury. They can act on virtually all subsets of immune cells through a spectrum of P2X ligand-gated ion channels and G protein-coupled P2Y receptors. Furthermore, ATP is rapidly degraded into adenosine by ectonucleotidases such as CD39 and CD73, and adenosine exerts additional regulatory effects through its own receptors. The resulting effect ranges from stimulation to tolerance depending on the amount and time courses of nucleotides released, and the balance between ATP and adenosine. This review identifies the various receptors involved in the different subsets of immune cells and their effects on the function of these cells