Poisson factorization for peer-based anomaly detection

Anomaly detection systems are a promising tool to identify compromised user credentials and malicious insiders in enterprise networks. Most existing approaches for modelling user behaviour rely on either independent observations for each user or on pre-defined user peer groups. A method is proposed based on recommender system algorithms to learn overlapping user peer groups and to use this learned structure to detect anomalous activity. Results analysing the authentication and process-running activities of thousands of users show that the proposed method can detect compromised user accounts during a red team exercise

Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Finding Groups in Gene Expression Data

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups

Angular dependence of domain wall resistivity in artificial magnetic domain structures

We exploit the ability to precisely control the magnetic domain structure of perpendicularly magnetized Pt/Co/Pt trilayers to fabricate artificial domain wall arrays and study their transport properties. The scaling behaviour of this model system confirms the intrinsic domain wall origin of the magnetoresistance, and systematic studies using domains patterned at various angles to the current flow are excellently described by an angular-dependent resistivity tensor containing perpendicular and parallel domain wall resistivities. We find that the latter are fully consistent with Levy-Zhang theory, which allows us to estimate the ratio of minority to majority spin carrier resistivities, rho-down/rho-up~5.5, in good agreement with thin film band structure calculations.Comment: 14 pages, 3 figure

Keyed Non-Parametric Hypothesis Tests

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $\kappa$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $\kappa$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.Comment: Paper published in NSS 201

2,6-Bis(diphenylphosphinosulfide)pyridine (L) as a facial terdentate ligand: synthesis and characterisation of the tricarbonylrhenium(I) complexes fac-[Re(CO)₃L]⁺ [Re₂(CO)₆(µ-X)₃]⁻ (X = Cl, Br or I) and fac-[Re(CO)₃L]⁺[SbF₆]⁻.

The halogenopentacarbonylrhenium(I) compounds react with 2,6-bis(diphenylphosphinosulfide) pyridine (L) under mild conditions to yield ionic complexes of general formulae, fac-[Re₂(CO)₃L]⁺ [Re2(CO) ₆(μ-X) )₃]⁻ (X=Cl, Br or I), in which the ligand adopts a facial terdentate bonding mode. A synthesis of [Re(CO)₃L]⁺ [SbF₆]⁻ was carried out to establish the presence of the cation, fac-[Re(CO)₃L]⁺, in the complexes. The character of the anions was confirmed by negative ion MALDI-TOF mass spectrometry. The cation is fluxional; the P–phenyl rings oriented towards the metal moiety exhibit restricted rotation at low temperature. The free energy of activation, ΔG †, for hindered rotation is ca. 47 kJ mol⁻¹ for all complexes. Solid-state₃ ¹P NMR data are reported for the free ligand and for the complexes, [Re(CO)₃L][SbF₆] and [Re(CO)₃L][Re₂(CO)₆(μ-X)₃] (X=Cl, Br or I)

Changepoint detection on a graph of time series

When analysing multiple time series that may be subject to changepoints, it is sometimes possible to specify a priori, by means of a graph G, which pairs of time series are likely to be impacted by simultaneous changepoints. This article proposes a novel Bayesian changepoint model for multiple time series that borrows strength across clusters of connected time series in G to detect weak signals for synchronous changepoints. The graphical changepoint model is further extended to allow dependence between nearby but not necessarily synchronous changepoints across neighbour time series in G. A novel reversible jump MCMC algorithm making use of auxiliary variables is proposed to sample from the graphical changepoint model. The merit of the proposed model is demonstrated via a changepoint analysis of real network authentication data from Los Alamos National Laboratory (LANL), with some success at detecting weak signals for network intrusions across users that are linked by network connectivity, whilst limiting the number of false alerts.Comment: 31 pages, 13 figure

Cosmic No Hair for Collapsing Universes

It is shown that all contracting, spatially homogeneous, orthogonal Bianchi cosmologies that are sourced by an ultra-stiff fluid with an arbitrary and, in general, varying equation of state asymptote to the spatially flat and isotropic universe in the neighbourhood of the big crunch singularity. This result is employed to investigate the asymptotic dynamics of a collapsing Bianchi type IX universe sourced by a scalar field rolling down a steep, negative exponential potential. A toroidally compactified version of M*-theory that leads to such a potential is discussed and it is shown that the isotropic attractor solution for a collapsing Bianchi type IX universe is supersymmetric when interpreted in an eleven-dimensional context.Comment: Extended discussion to include Kantowski-Sachs universe. In press, Classical and Quantum Gravit