Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

Numerical Relativity Injection Infrastructure

This document describes the new Numerical Relativity (NR) injection infrastructure in the LIGO Algorithms Library (LAL), which henceforth allows for the usage of NR waveforms as a discrete waveform approximant in LAL. With this new interface, NR waveforms provided in the described format can directly be used as simulated GW signals ("injections") for data analyses, which include parameter estimation, searches, hardware injections etc. As opposed to the previous infrastructure, this new interface natively handles sub-dominant modes and waveforms from numerical simulations of precessing binary black holes, making them directly accessible to LIGO analyses. To correctly handle precessing simulations, the new NR injection infrastructure internally transforms the NR data into the coordinate frame convention used in LAL.Comment: 20 pages, 2 figures, technical repor

Graph Laplacian for Image Anomaly Detection

Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer

Prospect for Charge Current Neutrino Interactions Measurements at the CERN-PS

Tensions in several phenomenological models grew with experimental results on neutrino/antineutrino oscillations at Short-Baseline (SBL) and with the recent, carefully recomputed, antineutrino fluxes from nuclear reactors. At a refurbished SBL CERN-PS facility an experiment aimed to address the open issues has been proposed [1], based on the technology of imaging in ultra-pure cryogenic Liquid Argon (LAr). Motivated by this scenario a detailed study of the physics case was performed. We tackled specific physics models and we optimized the neutrino beam through a full simulation. Experimental aspects not fully covered by the LAr detection, i.e. the measurements of the lepton charge on event-by-event basis and their energy over a wide range, were also investigated. Indeed the muon leptons from Charged Current (CC) (anti-)neutrino interactions play an important role in disentangling different phenomenological scenarios provided their charge state is determined. Also, the study of muon appearance/disappearance can benefit of the large statistics of CC muon events from the primary neutrino beam. Results of our study are reported in detail in this proposal. We aim to design, construct and install two Spectrometers at "NEAR" and "FAR" sites of the SBL CERN-PS, compatible with the already proposed LAr detectors. Profiting of the large mass of the two Spectrometers their stand-alone performances have also been exploited.Comment: 70 pages, 38 figures. Proposal submitted to SPS-C, CER

Physics Results from RICH Detectors

RICH detectors have become extraordinarily useful. Results include measurement of solar neutrino rates, evidence for neutrino oscillations, measurement of TeV gamma-rays from gravitational sources, properties of QCD, charm production and decay, and measurement of the CKM matrix elements Vcs, Vcb and Vub. A new value |Vub/Vcb|=0.087+/-0.012 is determined.Comment: Invited talk at ``The 3rd International Workshop on Ring Imaging Cherenkov Detectors," Ein-Gedi, Dead-Sea, Israel, Nov. 15-20, 1998, 21 pages, 22 fig