Повышение надежности идентификации пользователей компьютерных систем по клавиатурному почерку

С целью повышения надежности систем идентификации пользователей компьютерных систем представляется удачной идея комбинации стандартной парольной защиты с методом идентификации пользователя по клавиатурному почерку. Приводится сравнительный анализ существующих методов. В зависимости от используемых методов в детекторе новизны можно получить увеличение точности систем распознавания по клавиатурному почерку

Повышение надежности идентификации пользователей компьютерных систем по клавиатурному почерку

С целью повышения надежности систем идентификации пользователей компьютерных систем представляется удачной идея комбинации стандартной парольной защиты с методом идентификации пользователя по клавиатурному почерку. Приводится сравнительный анализ существующих методов. В зависимости от используемых методов в детекторе новизны можно получить увеличение точности систем распознавания по клавиатурному почерку

Novelty Detection in Airport Baggage Conveyor Gear-Motors Using Synchro-Squeezing Transform and Self-Organizing Maps

A powerful continuous wavelet transform based signal processing tool named Synchro-squeezing transform (SST) has recently emerged in the context of non-stationary signal processing. Founded upon the premise of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane. Additionally, it can also extract the individual components of a non-stationary multi-component signal, which makes it attractive for rotating machinery signals. This work utilizes the decomposing power of SST transform to extract useful components from gear-motor signals in relevant sub-bands, followed by the application of standard rotating machinery condition indicators. For timely detection of faults in airport baggage conveyor gear-motors, a novelty detection technique based on the concept of self-organizing maps (SOM) is applied on the condition indicators. This approach promises improved anomaly detection performance than that can be achieved by applying condition indicators and SOM directly to the inherently complex raw-data. Data collected from conveyor gear-motors provides a test bed to demonstrate the efficacy of the proposed approach

Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks

This paper introduces the DANTE project: Detection of Anomalies and Novelties in Time sEries with self-organizing networks. The goal of this project is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. For this purpose, we first describe three standard clustering-based approaches which uses well-known self-organizing neural architectures, such as the SOM and the Fuzzy ART algorithms, and then present a novel approach based on the Operator Map (OPM) network. The OPM is a generalization of the SOM where neurons are regarded as temporal filters for dynamic patters. The OPM is used to build local adaptive filters for a given nonstationary time series. Non-parametric confidence intervals are then computed for the residuals of the local models and used as decision thresholds for detecting novelties/anomalies. Computer simulations are carried out to compare the performances of the aforementioned algorithms

Использование динамических биометрических характеристик для идентификации пользователя в сети

Possibilities of user identification are analyses on the features of the keystroke dynamics and dynamics of work with a mouse during the input of password during work in network applications and services. The scenarios of client-server realization of the systems of biometric authentication are presented on dynamic biometric signs. The features of every scenario are considered for a reasonable choice in certain situations.Проанализированы возможности идентификации пользователя по особенностям клавиатурного почерка и динамике работы с мышью во время ввода пароля при работе в сетевых приложениях и сервисах. Представлены сценарии клиент-серверной реализации систем биометрической идентификации по динамическим биометрическим признакам. Рассмотрены особенности каждого сценария для обоснованного выбора в конкретных ситуациях.Проаналізовані можливості ідентифікації користувача за особливостями клавіатурного почерку та динаміці роботи з мишею під час введення паролю при роботі в мережевих додатках та сервісах. Представлені сценарії клієнт-серверної реалізації систем біометричної ідентифікації за динамічними біометричними ознаками. Розглянуті особливості кожного сценарію для здійснення обґрунтованого вибору в конкретних ситуаціях

Использование динамических биометрических характеристик для идентификации пользователя в сети

Possibilities of user identification are analyses on the features of the keystroke dynamics and dynamics of work with a mouse during the input of password during work in network applications and services. The scenarios of client-server realization of the systems of biometric authentication are presented on dynamic biometric signs. The features of every scenario are considered for a reasonable choice in certain situations.Проанализированы возможности идентификации пользователя по особенностям клавиатурного почерка и динамике работы с мышью во время ввода пароля при работе в сетевых приложениях и сервисах. Представлены сценарии клиент-серверной реализации систем биометрической идентификации по динамическим биометрическим признакам. Рассмотрены особенности каждого сценария для обоснованного выбора в конкретных ситуациях.Проаналізовані можливості ідентифікації користувача за особливостями клавіатурного почерку та динаміці роботи з мишею під час введення паролю при роботі в мережевих додатках та сервісах. Представлені сценарії клієнт-серверної реалізації систем біометричної ідентифікації за динамічними біометричними ознаками. Розглянуті особливості кожного сценарію для здійснення обґрунтованого вибору в конкретних ситуаціях

Novelty detection with self-organizing maps for autonomous extraction of salient tracking features

International audienceIn the image processing field, many tracking algorithms rely on prior knowledge like color, shape or even need a database of the objects to be tracked. This may be a problem for some real world applications that cannot fill those prerequisite. Based on image compression techniques, we propose to use Self-Organizing Maps to robustly detect novelty in the input video stream and to produce a saliency map which will outline unusual objects in the visual environment. This saliency map is then processed by a Dynamic Neural Field to extract a robust and continuous tracking of the position of the object. Our approach is solely based on unsupervised neural networks and does not need any prior knowledge, therefore it has a high adaptability to different inputs and a strong robustness to noisy environments

Automated Fault Diagnosis in Rotating Machinery

Rotating machinery are an important part of industrial equipment. Their components are subjected to harsh operating environments, and hence experience significant wear and tear. It is necessary that they function efficiently all the time in order to avoid significant monetary losses and down-time. Monitoring the health of such machinery components has become an essential part in many industries to ensure their continuous operation and avoiding loss in productivity. Traditionally, signal processing methods have been employed to analyze the vibration signals emitted from rotating machines. With time, the complexity of machinery components has increased, which makes the process of condition monitoring complex and time consuming, and consequently costly. Hence, a paradigm shift in condition monitoring methods towards data-driven approaches has recently taken place towards reducing complexity in estimation, where the monitoring of machinery is focused on purely data-driven methods. In this thesis, a novel data-driven framework to condition monitoring of gearbox is studied and illustrated using simulated and experimental vibration signals. This involves analyzing the signal, deriving feature sets and using machine learning algorithms to discern the condition of machinery. The algorithm is implemented on data from a drivetrain dynamics simulator (DDS), equipment designed by Spectraquest Inc. for academic and industrial research purposes. Datasets from pristine state and faulty gearboxes are collected and the algorithms are tested against this data. This framework has been developed to facilitate automated monitoring of machinery in industries, thus reducing the need for manual supervision and interpretation