Search results for "processi"
showing 10 items of 9638 documents
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…
Seismic behavior of structures equipped with variable friction dissipative (VFD) systems
2021
Usually, to mitigate the stresses in framed structures, different strategies are used. Among them, base isolation, viscous/friction/metallic yielding dampers and tuned mass dumpers have been widely investigated. Fluid Viscous Dampers (FVD) probably result the most diffused for the simplicity in the applications. However, these type of dampers request limited interstorey drifts to avoid dangerous effects. Further, they have an elevate cost. On the contrary, friction dampers are not so expensive but request high interstorey drifts to give a significant contribute in the dissipation of energy during an earthquake. In this paper an approach for the energy dissipation by friction, modified with …
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…
Anti-Listeria activity of lactic acid bacteria in two traditional Sicilian cheeses
2017
<em>Listeria monocytogenes</em> is a pathogen frequently found in dairy products, and its growth is difficult to control. Bacteriocinlike inhibitory substances (BLIS), produced by lactic acid bacteria (LAB), having proven <em>in vitro</em> anti-<em>Listeria</em> activity, could provide an innovative approach to control <em>L. monocytogenes</em>; however, this application needs to be evaluated <em>in vivo</em>. In this study, twenty LAB strains isolated from different Sicilian dairy environments were tested for control of growth of <em>L. monocytogenes</em> in three different experimental trials. First, raw and UHT milk …
Mineral analysis of human diets by spectrometry methods
2016
Abstract Mineral element determination in human diets is very important for human health, due to the presence in foods of essential and toxic elements or their incorporation in the manipulation and cooking food process. Different instrumental techniques have been used to determine mineral elements in human diets, but atomic spectroscopy and mass spectrometry based ones are the most commonly employed. Sampling procedures for diet analysis are the main critically step for mineral element determination, being employed different standardised protocols. This review summarised critically the state-of-the-art of mineral analysis in human diets, considering sampling, sample preparation and determin…
Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a…
2017
Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils and beans), spot urine samples from a subcohort from the PREDIMED study were stratified, using a validated food frequency questionnaire. Non-pulse consumers (≤ 4 g/day of pulse intake) and habitual pulse consumers (≥ 25 g/day of pulse intake) were analysed using a 1H-NMR metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through…
In vitro bioavailability of iron and calcium in cereals and derivatives: A review
2016
Cereals are a staple food in both developed and developing countries, and are considered to be the best vehicle for iron and calcium fortification, as an important strategy for combating dietary deficits. Inadequate dietary intake of iron and calcium is related to a number of disease conditions such as anemia, osteoporosis, hypertension, and different cancers. From a nutritional point of view, it is interesting to know not only the amount of minerals consumed, but also their bioavailability. The present study reviews the current knowledge on the in vitro bioavailability of iron and calcium in cereals, placing emphasis on the methodologies used and on the influence of dietary factors and foo…