Possibilistic classifiers for numerical data

International audienceNaive Bayesian Classifiers, which rely on independence hypotheses, together with a normality assumption to estimate densities for numerical data, are known for their simplicity and their effectiveness. However, estimating densities, even under the normality assumption, may be problematic in case of poor data. In such a situation, possibility distributions may provide a more faithful representation of these data. Naive Possibilistic Classifiers (NPC), based on possibility theory, have been recently proposed as a counterpart of Bayesian classifiers to deal with classification tasks. There are only few works that treat possibilistic classification and most of existing NPC deal only with categorical attributes. This work focuses on the estimation of possibility distributions for continuous data. In this paper we investigate two kinds of possibilistic classifiers. The first one is derived from classical or flexible Bayesian classifiers by applying a probability–possibility transformation to Gaussian distributions, which introduces some further tolerance in the description of classes. The second one is based on a direct interpretation of data in possibilistic formats that exploit an idea of proximity between data values in different ways, which provides a less constrained representation of them. We show that possibilistic classifiers have a better capability to detect new instances for which the classification is ambiguous than Bayesian classifiers, where probabilities may be poorly estimated and illusorily precise. Moreover, we propose, in this case, an hybrid possibilistic classification approach based on a nearest-neighbour heuristics to improve the accuracy of the proposed possibilistic classifiers when the available information is insufficient to choose between classes. Possibilistic classifiers are compared with classical or flexible Bayesian classifiers on a collection of benchmarks databases. The experiments reported show the interest of possibilistic classifiers. In particular, flexible possibilistic classifiers perform well for data agreeing with the normality assumption, while proximity-based possibilistic classifiers outperform others in the other cases. The hybrid possibilistic classification exhibits a good ability for improving accuracy

On the Use of Clustering in Possibilistic Decision Tree Induction

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Using hierarchical statistical analysis and deep neural networks to detect covert timing channels

Covert timing channels provide a mechanism to leak data across different entities. Manipulating the timing between packet arrivals is a well-known example of such approach. The time based property makes the detection of the hidden messages impossible by traditional security protecting mechanisms such as proxies and firewalls. This paper introduces a new generic hierarchical-based model to detect covert timing channels. The detection process consists of the analysis of a set of statistical metrics at consecutive hierarchical levels of the inter-arrival times flows. The statistical metrics considered are: mean, median, standard deviation, entropy, Root of Average Mean Error (RAME). A real statistical metrics timing channel dataset of covert and overt channel instances is created. The generated dataset is set to be either flat where the statistical metrics are calculated on all flows of data or hierarchal (5 levels of hierarchy were considered) where the statistical metrics are computed on sub parts of the flow as well. Following this method, 5 different datasets were generated, and used to train/test a deep neural network based model. Performance results about accuracy and model training time showed that the hierarchical approach outperforms the flat one by 4 to 10 percent (in terms of accuracy) and was able to achieve short model training time (in terms of seconds). When compared to the Support Vector Machine (SVM) classifier, the deep neural network achieved a better accuracy level (about 2.3% to 12% depends on the used kernel) and significantly shorter model training time (few seconds versus few 100’s of seconds). This paper also explores the importance of the used metrics in each level of the detection process

PROGRESS ON THE SRF LINAC DEVELOPMENTS FOR THE IFMIF- LIPAC PROJECT

Abstract In the framework of the International Fusion Materials Irradiation Facility (IFMIF), which consists of two high power accelerator drivers, each delivering a 125 mA deuteron beam at 40 MeV in CW, a Linear IFMIF Prototype Accelerator (LIPAc), is presently under design and realization for the first phase of the project [1]. This accelerator prototype includes a Superconducting RadioFrequency Linac (SRF Linac), which is designed for the transportation and focalization of the deuteron beam up to 9 MeV. This SRF Linac is a large cryomodule of ~6 m long, working at 4.4 K and at the frequency of 175 MHz in continuous wave. It is mainly composed of 8 low-beta Half-Wave Resonators (HWR), 8 Solenoid Packages and 8 RF Power Couplers. This paper focuses on the recent developments and changes made on the SRF Linac design: following the abandon of the HWR frequency tuning system, initially based on a plunger located inside the central region of the resonator, a new external tuning system has been designed, implying a complete redesign of the resonator and consequently impacting the cryomodule lattice. The recent changes in the design are presented in this paper. In addition, cold tests were performed on a HWR prototype and cold tests results of the magnets prototypes are also presented