Search results for " Process"
showing 10 items of 17204 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…
Two-qubit entanglement dynamics for two different non-Markovian environments
2009
We study the time behavior of entanglement between two noninteracting qubits each immersed in its own environment for two different non-Markovian conditions: a high-$Q$ cavity slightly off-resonant with the qubit transition frequency and a nonperfect photonic band-gap, respectively. We find that revivals and retardation of entanglement loss may occur by adjusting the cavity-qubit detuning, in the first case, while partial entanglement trapping occurs in non-ideal photonic-band gap.
An in vitro cyclic fatigue resistance comparison of conventional and new generation nickel-titanium rotary files
2018
Background New designs and processing of Niquel-Titanium (NiTi) have been introduced to increase resistance to cyclic fatigue. The purpose of this study was to compare the cyclic fatigue resistance of 3 NiTi rotary instruments, ProTaper Next (PTN; Dentsply Maillefer, Ballaigues, Switzerland), Profile Vortex Blue (PVB; Dentsply Tulsa Dental, Tulsa, OK, USA) and ProTaper Universal (PTU; Dentsply Maillefer, Ballaigues, Switzerland). Material and Methods A cyclic fatigue test was conducted operating instruments from ProTaper Next X2, Profile Vortex Blue 25.06 and ProTaper F2. A total of 234 instruments were rotated in 2 simulated stainless steel curved canals with different angles of curvature …
SPECTR
2018
Modern high throughput sequencing platforms can produce large amounts of short read DNA data at low cost. Error correction is an important but time-consuming initial step when processing this data in order to improve the quality of downstream analyses. In this paper, we present a Scalable Parallel Error CorrecToR designed to improve the throughput of DNA error correction for Illumina reads on various parallel platforms. Our design is based on a k-spectrum approach where a Bloom filter is frequently probed as a key operation and is optimized towards AVX-512-based multi-core CPUs, Xeon Phi many-cores (both KNC and KNL), and heterogeneous compute clusters. A number of architecture-specific opt…
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 …