Search results for "detection"
showing 10 items of 2543 documents
Design Issues and Sample Size when Exposure Measurement is Inaccurate
2001
AbstractMeasurement error often leads to biased estimates and incorrect tests in epidemiological studies. These problems can be corrected by design modifications which allow for refined statistical models, or in some situations by adjusted sample sizes to compensate a power reduction. The design options are mainly an additional replication or internal validation study. Sample size calculations for these designs are more complex, since usually there is no unique design solution to obtain a prespecified power. Thus, additionally to a power requirement, an optimal design should also fulfill the criteria of minimizing overall costs. In this review corresponding strategies and formulae are descr…
State of the Art Literature Review on Network Anomaly Detection
2018
As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additi…
A Novel Method for Detecting APT Attacks by Using OODA Loop and Black Swan Theory
2018
Advanced Persistent Threat(APT) attacks are a major concern for the modern societal digital infrastructures due to their highly sophisticated nature. The purpose of these attacks varies from long period espionage in high level environment to causing maximal destruction for targeted cyber environment. Attackers are skilful and well funded by governments in many cases. Due to sophisticated methods it is highly important to study proper countermeasures to detect these attacks as early as possible. Current detection methods under-performs causing situations where an attack can continue months or even years in a targeted environment. We propose a novel method for analysing APT attacks through OO…
A Novel Deep Learning Stack for APT Detection
2019
We present a novel Deep Learning (DL) stack for detecting Advanced Persistent threat (APT) attacks. This model is based on a theoretical approach where an APT is observed as a multi-vector multi-stage attack with a continuous strategic campaign. To capture these attacks, the entire network flow and particularly raw data must be used as an input for the detection process. By combining different types of tailored DL-methods, it is possible to capture certain types of anomalies and behaviour. Our method essentially breaks down a bigger problem into smaller tasks, tries to solve these sequentially and finally returns a conclusive result. This concept paper outlines, for example, the problems an…
State of the Art Literature Review on Network Anomaly Detection with Deep Learning
2018
As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additi…
Scratches Removal in Digitised Aerial Photos Concerning Sicilian Territory
2007
In this paper we propose a fast and effective method to detect and restore scratches in aerial photos from a photographic archive concerning Sicilian territory. Scratch removal is a typical problem for old movie films but similar defects can be seen in still images. Our solution is based on a semiautomatic detection process and an unsupervised restoration algorithm. Results are comparable with those obtained with commercial restoration tools.
Drop-on-demand sample introduction system coupled with the flowing atmospheric-pressure afterglow for direct molecular analysis of complex liquid mic…
2012
One of the fastest developing fields in analytical spectrochemistry in recent years is ambient desorption/ionization mass spectrometry (ADI-MS). This burgeoning interest has been due to the demonstrated advantages of the method: simple mass spectra, little or no sample preparation, and applicability to samples in the solid, liquid, or gaseous state. One such ADI-MS source, the flowing atmospheric-pressure afterglow (FAPA), is capable of direct analysis of solids just by aiming the source at the solid surface and sampling the produced ions into a mass spectrometer. However, direct introduction of significant volumes of liquid samples into this source has not been possible, as solvent loads c…
Towards comprehensive non-target screening using heart-cut two-dimensional liquid chromatography for the analysis of organic atmospheric tracers in i…
2021
Abstract Non-target screening of secondary organic aerosol compounds in ice cores is used to reconstruct atmospheric conditions and sources and is a valuable tool to elucidate the chemical profiles of samples with the aim to obtain as much information as possible from one mass spectrometric measurement. The coupling of mass spectrometry to chromatography limits the results of a non-target screening to signals of compounds within a certain polarity range based on the utilized stationary phases of the columns. Comprehensive two-dimensional liquid chromatography (LCxLC) introduces a second column of different functionality to enable the analysis of a broader range of analytes. Conventional LCx…
Nanosensor Devices for CBRN-Agents Detection: Theory and Design
2018
Pressing challenges of recent decades, associated with agents that are aggressive towards humans – substances and radiation of chemical, biological, radiological, and nuclear (CBRN) agents – require scientific and technological responses. These responses lie in the areas of agent detection and protection from them. The mentioned bio destructive agents can be divided into 2 groups: (1) chemical and biochemical, and (2) radiative (leading to chemical destruction of biomass). In this study, we consider models of universal track nanosensors that are capable of producing a correlated electrical response to the flow of active agents.
A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in L…
2021
Background The COVID-19 outbreak, an event of global concern, has provided scientists the opportunity to use mathematical modeling to run simulations and test theories about the pandemic. Objective The aim of this study was to propose a full-scale individual-based model of the COVID-19 outbreak in Lombardy, Italy, to test various scenarios pertaining to the pandemic and achieve novel performance metrics. Methods The model was designed to simulate all 10 million inhabitants of Lombardy person by person via a simple agent-based approach using a commercial computer. In order to obtain performance data, a collision detection model was developed to enable cluster nodes in small cells that can b…