Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

We have witnessed an increase in the availability of data from diverse sources over the past few years. Cloud computing, big data and Internet-of-Things (IoT) are distinctive cases of such an increase which demand novel approaches for data analytics in order to process and analyze huge volumes of data for security and business use. Cloud computing has been becoming popular for critical structure IT mainly due to cost savings and dynamic scalability. Current offerings, however, are not mature enough with respect to stringent security and resilience requirements. Mechanisms such as anomaly detection hybrid systems are required in order to protect against various challenges that include network based attacks, performance issues and operational anomalies. Such hybrid AI systems include Neural Networks, blackboard systems, belief (Bayesian) networks, case-based reasoning and rule-based systems and can be implemented in a variety of ways. Traffic in the cloud comes from multiple heterogeneous domains and changes rapidly due to the variety of operational characteristics of the tenants using the cloud and the elasticity of the provided services. The underlying detection mechanisms rely upon measurements drawn from multiple sources. However, the characteristics of the distribution of measurements within specific subspaces might be unknown. We argue in this paper that there is a need to cluster the observed data during normal network operation into multiple subspaces each one of them featuring specific local attributes, i.e. granules of information. Clustering is implemented by the inference engine of a model hybrid NN system. Several variations of the so-called value-difference metric (VDM) are investigated like local histograms and the Canberra distance for scalar attributes, the Jaccard distance for binary word attributes, rough sets as well as local histograms over an aggregate ordering distance and the Canberra measure for vectorial attributes. Low-dimensional subspace representations of each group of points (measurements) in the context of anomaly detection in critical cloud implementations is based upon VD metrics and can be either parametric or non-parametric. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate ordered sets of objects featuring VD metrics (as obtained from distributed network measurements) is proposed. Each node of the SOFM stands for a structured local distribution of such objects within the input space. The so-called Neighborhood-based Outlier Factor (NOOF) is defined for such reduced/aggregate ordered sets of objects as a value-difference metric of histogrammes. Measurements that do not belong to local distributions are detected as anomalies, i.e. outliers of the trained SOFM. Several methods of subspace clustering using Expectation-Maximization Gaussian Mixture Models (a parametric approach) as well as local data densities (a non-parametric approach) are outlined and compared against the proposed method using data that are obtained from our cloud testbed in emulated anomalous traffic conditions. The results—which are obtained from a model NN system—indicate that the proposed method performs well in comparison with conventional techniques

Chiral Symmetry Restoration and Dileptons in Relativistic Heavy-Ion Collisions

The current theoretical status in the analysis and interpretation of low-mass dilepton measurements in (ultra-) relativistic heavy-ion experiments is reviewed. Special emphasis is put on potential signals of (partial) restoration of dynamically broken chiral symmetry in a hot and dense hadronic medium. It follows from chiral symmetry alone that parity partners of hadronic correlation functions must become identical when the symmetry is restored. The assessment of medium effects in the vector channel, which governs the dilepton production, thus necessitates a simultaneous treatment of the vector and axialvector degrees of freedom. While significant progress in this respect has been made some open questions remain in establishing a rigorous link in the mass region below 1 GeV. From the present calculations a suggestive 'quark-hadron duality' emerges near the phase boundary. It implies substantial medium effects in the dilepton signal from the hadronic phase which smoothly matches a perturbative description within the plasma phase.Comment: 164 pages LaTeX including 88 eps-/ps-figures, Review Article to appear in Adv. Nucl. Phy

Semantics of the VDM Real-Time Dialect

All formally defined languages need to be given an unambiguous semantics such that the meaning of all models expressed using the language is clear. In this technical report a semantic model is provided for the Real-Time dialect of the Vienna Development Method (VDM). This builds upon both the formal semantics provided for the ISO standard VDM Specification Language, and on other work on the core of the VDM-RT notation. Although none of the VDM dialects are executable in general, the primary focus of the work presentedhere is on the executable subset. This focus is result of parallel work on an interpreter implementation for VDM-RT that chooses one of the pos-sible interpretations of a given model that is expressed in VDM-RT, based on the semantics presented here

New mechanism producing axions in the AQN model and how the CAST can discover them

We advocate the idea that there is a fundamentally new mechanism for the axion production in the Sun, which has never been discussed previously in the literature. This novel mechanism of the axion production is based on the so-called Axion Quark Nugget (AQN) Dark Matter Model. These axions will be produced in addition to well studied axions emitted due to the Primakoff effect. The AQN model was originally invented as a natural explanation of the observed ratio $\Omega_{\rm dark} \sim \Omega_{\rm visible}$ when the DM and visible matter densities assume the same order of magnitude values, irrespectively to the axion mass $m_a$ or initial misalignment angle $\theta_0$. This model, without adjustment of any parameters, reproduces reasonably the intensity of the extreme UV (EUV) radiation from the solar corona as a result of the AQN annihilation events with the solar material. This extra energy released in corona represents a resolution, within AQN framework, a long standing puzzle known in the literature as the "solar corona heating mystery". The same annihilation events also produce the relativistic axions. This represents a new mechanism of the axion production, and constitutes the main subject of this work. The flux of these axions is unambiguously fixed in this model and expressed in terms of the EUV luminosity from corona. We also compute the spectral properties of these axions and make few comments on the potentials for the discovery of these solar axions by the upgraded CAST (CERN Axion Solar Axion) experiment.Comment: matches the published versio

Type theory as a framework for modelling and programming

In the context provided by the proceedings of the UVMP track of ISoLA 2016, we propose Type Theory as a suitable framework for both modelling and programming. We show that it fits most of the requirements put forward on such frameworks by Broy et al. and discuss some of the objections that can be raised against it