Mitigating Coordinated Call Attacks On VoIP Networks Using Hidden Markov Model

Abstract This paper presents a 2-tier scheme for mitigating coordinated call attacks on VoIP networks. Call interaction pattern was considered using talk and salient periods in a VoIP call conversation. At the first-tier, Short Term Energy algorithm was used for call interaction feature extraction and at the second-tier Hidden Markov Model was used for caller legitimacy recognition. Data of VoIP call conversations were collated and analyzed to extract distinctive features in VoIP call interaction pattern to ascertain the legitimacy of a caller against coordinated call attacker. The performance metrics that was used are; False Error Rate (FER), Specificity, Detection Accuracy and Throughput. Several experiments were conducted to see how effective the mitigating scheme is, as the scheme acts as a proxy server to Session Initiation Protocol (SIP) server. The experiments show that; when the VoIP server is under coordinated call attack without a mitigating scheme only 15.2% of legitimate VoIP users had access to the VoIP network and out of which about half of the legitimate users had their calls dropped before completion, while with the 2-tier mitigating scheme, when the VoIP server is under coordinated call attacks over 90.3% legitimate VoIP callers had their calls through to completio

Statistical Inference in Open Quantum Systems

This thesis concerns the statistical analysis of open quantum systems subject to an external and non-stationary perturbation. In the first paper, a generalization of the explicit-duration hidden Markov models (EDHMM) which takes into account the presence of sparse data is presented. Introducing a kernel estimator in the estimation procedure increases the accuracy of the estimates, and thus allows one to obtain a more reliable information about the evolution of the unobservable system. A generalization of the Viterbi algorithm to EDHMM is developed. In the second paper, we develop a Markov Chain Monte Carlo (MCMC) procedure for estimating the EDHMM. We improve the flexibility of our formulation by adopting a Bayesian model selection procedure which allows one to avoid a direct specification of the number of states of the hidden chain. Motivated by the presence of sparsity, we make use of a non-parametric estimator to obtain more accurate estimates of the model parameters. The formulation presented turns out to be straightforward to implement, robust against the underflow problem and provides accurate estimates of the parameters. In the third paper, an extension of the Cramér-Rao inequality for quantum discrete parameter models is derived. The latter are models in which the parameter space is restricted to a finite set of points. In some estimation problems indeed, theory provides us with additional information that allow us to restrict the parameter space to a finite set of points. The extension presented sets the ultimate accuracy of an estimator, and determines a discrete counterpart of the quantum Fisher information. This is particularly useful in many experiments in which the parameters can assume only few different values: for example, the direction which the magnetic field points to. We also provide an illustration related to a quantum optics problem

Aplicación de tecnologías de segmentación de audio y reconocimiento automático de dialecto para la obtención de información de diálogos contenidos en audio

El interés de la comunidad científica en la identificación de contenidos audiovisuales ha crecido considerablemente en los últimos años, debido a la necesidad de ejecutar procesos automáticos de clasificación y monitoreo del cada vez mayor contenido transmitido por diferentes medios como televisión, radio e internet. En este artículo se propone una arquitectura para la extracción de información a partir de audio, con la finalidad de aplicarlo al análisis de contenidos televisivos en el contexto ecuatoriano. Para esto, se definen dos servicios, un servicio de segmentación de audio y un servicio de transcripción. El servicio de segmentación identifica y extrae los segmentos de audio que contienen narrativa, música, o narrativa sobre música. Mientras que, el servicio de transcripción hace un reconocimiento de los segmentos de tipo narrativa para obtener su contenido como texto. Estos servicios y las herramientas que los conforman han sido evaluados con el fin de medir su rendimiento y, en el caso de las herramientas usadas, definir cuál de estas es la que mejor se ajusta a la definición de la arquitectura. Los resultados de las evaluaciones realizadas sobre la arquitectura propuesta demuestran que la construcción de un sistema de reconocimiento de habla que haga uso de distintas herramientas de código abierto existentes ofrece un mayor nivel de precisión que un servicio de transcripción de disposición general.The interest of the scientific community in the identification of audiovisual content has grown considerably in recent years, due to the need to execute automatic classification and monitoring processes on the increasing content broadcasted by different media such as television, radio and internet. This article proposes an architecture for extracting information from audio, with the purpose of applying it to the analysis of television contents in the Ecuadorian context. For this, two services are defined, an audio segmentation service and a transcription service. The segmentation service identifies and extracts audio segments containing speech, music, or speech with musical background. Whereas, the transcription service recognizes the speech segments to obtain its content as text. These services and the tools that conform them have been evaluated in order to measure their performance and, in the case of the tools used, to define which of these is the one that best fits the definition of the architecture. The results of the evaluations carried out on the proposed architecture demonstrate that the construction of a speech recognition system, that makes use of different existing open source tools, offers a higher level of precision than a general availability transcription service.Ingeniero de SistemasCuenc

An online EM algorithm in hidden (semi-)Markov models for audio segmentation and clustering

International audienceAudio segmentation is an essential problem in many audio signal processing tasks, which tries to segment an audio signal into homogeneous chunks. Rather than separately finding change points and computing similarities between segments, we focus on joint segmentation and clustering, using the framework of hidden Markov and semi-Markov models. We introduce a new incremental EM algorithm for hidden Markov models (HMMs) and show that it compares favorably to existing online EM algorithms for HMMs. We present results for real-time segmentation of musical notes and acoustic scenes

Clustering incrémental de signaux audio

National audienceThis report aims to study different methods of online clustering, mainly appliedto audio signals. We will first detail the state-of-the art algorithms for clustering, aswell as the theory behind them. Then we will extend this methods to incrementalclustering, and present different online algorithms. These algorithms are based on thehidden markov models, which are classic art representations of data and hidden states insignal processing, and hidden semi-markov models, which extend them to a semi-markovrepresentation of the states. We present this within the context of audio segmentation– the task of segmenting audio sources in homogenous chunks – and classification – thetask of identifying these chunks – applied to audio event detection. We will also setan experimental protocol, with a view to evaluate them and compare the result to astate-of-the art algorithm for the same task.Ce rapport de stage vise à l’étude de méthodes de partitionnement incrémentales,principalement appliquées à des signaux audio. Nous détaillons tous d’abord les algo-rithmes de partitionnement classiques de la littérature, ainsi que les bases théoriquespermettant d’y aboutir. Puis nous présentons des méthodes étendant ces algorithmesde partitionnement au calcul en ligne. Nous utilisons pour cela les hmm, qui sont unemodélisation classique de données et états latents en traitement du signal. Nous utili-sons aussi les hsmm, qui sont une extension des hmm, permettant une représentationsemi-markovienne des états cachés. Ces algorithmes sont présentés dans le cadre de lasegmentation audio – visant à séparer un fichier en fragments homogènes – et de laclassification – visant à identifier les différents fragments – pouvant être appliquées àla détection d’événements sonores. On proposera aussi un protocole d’évaluation pources méthodes, afin de comparer leurs performances par rapport à une autre méthode del’état de l’art en leur faisant effectuer les mêmes tâches