Search results for "image processing"
showing 10 items of 3285 documents
Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation
2016
Abstract Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray…
Intelligent Constructing Exact Tolerance Limits for Prediction of Future Outcomes Under Parametric Uncertainty
2021
The problem of constructing one-sided exact statistical tolerance limits on the kth order statistic in a future sample of m observations from a distribution of log-location-scale family on the basis of an observed sample from the same distribution is considered. The new technique proposed here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The exact tolerance limits on order statistics associated with sampling from underlying distributions can be found easily and quickly making tables, simulation, Monte Carlo estimated percen…
A New Intelligent Technique of Constructing Optimal Airline Seat Protection Levels for Multiple Nested Fare Classes of Single-Leg Flights
2019
A new, rigorous formulation of the optimization problem of airline seat protection levels for multiple nested fare classes is presented. A number of results useful for practical application are obtained. A numerical example is given.
Data Sources Handling for Emergency Management: Supporting Information Availability and Accessibility for Emergency Responders
2017
Information is an essential component for better emergency response. Although a lot of information being available at various places during any kind of emergency, many emergency responders (ERs) use only a limited amount of the available information. The reason for this is that the available information heterogeneously distributed, in different formats, and ERs are unable to get access to the relevant information. Moreover, without having access to the needed information, many emergency responders are not able to obtain a sufficient understanding of the emergency situation. Consequently, a lot of time is being used to search for the needed information and poor decisions may be made. Therefo…
HyperWall: A Hypervisor for Detection and Prevention of Malicious Communication
2020
Malicious programs vary widely in their functionality, from key-logging to disk encryption. However, most malicious programs communicate with their operators, thus revealing themselves to various security tools. The security tools incorporated within an operating system are vulnerable to attacks due to the large attack surface of the operating system kernel and modules. We present a kernel module that demonstrates how kernel-mode access can be used to bypass any security mechanism that is implemented in kernel-mode. External security tools, like firewalls, lack important information about the origin of the intercepted packets, thus their filtering policy is usually insufficient to prevent c…
Deep in the Dark: A Novel Threat Detection System using Darknet Traffic
2019
This paper proposes a threat detection system based on Machine Learning classifiers that are trained using darknet traffic. Traffic destined to Darknet is either malicious or by misconfiguration. Darknet traffic contains traces of several threats such as DDoS attacks, botnets, spoofing, probes and scanning attacks. We analyse darknet traffic by extracting network traffic features from it that help in finding patterns of these advanced threats. We collected the darknet traffic from the network sensors deployed at SURFnet and extracted several network-based features. In this study, we proposed a framework that uses supervised machine learning and a concept drift detector. Our experimental res…
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
2019
Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…
On Detection of Network-Based Co-residence Verification Attacks in SDN-Driven Clouds
2017
Modern cloud environments allow users to consume computational and storage resources in the form of virtual machines. Even though machines running on the same cloud server are logically isolated from each other, a malicious customer can create various side channels to obtain sensitive information from co-located machines. In this study, we concentrate on timely detection of intentional co-residence attempts in cloud environments that utilize software-defined networking. SDN enables global visibility of the network state which allows the cloud provider to monitor and extract necessary information from each flow in every virtual network in online mode. We analyze the extracted statistics on d…
Image-Evoked Affect and its Impact on Eeg-Based Biometrics
2019
Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…