16 research outputs found
PDF uncertainties in Higgs production at hadron colliders
No full textUsing the new schemes provided by the CTEQ and MRST collaborations and by Alekhin, we analyse the uncertainties due to the parton distribution functions (PDFs) on the next-to-leading-order cross sections of the four main production processes of the Standard Model Higgs boson at the LHC and the Tevatron. In the Higgs mass range where the production rates are large enough, the spread in the uncertainties when the three sets of PDFs are compared is of about 15% in all processes and at both colliders. However, within one given set of PDFs, the deviations from the values obtained with the reference sets are much smaller, being of %), except in the gluon--gluon fusion mechanism at relatively large Higgs boson masses, where they can reach the level of 10% (15%) at the LHC (Tevatron).Using the new schemes provided by the CTEQ and MRST collaborations and by Alekhin, we analyse the uncertainties due to the parton distribution functions (PDFs) on the next-to-leading-order cross sections of the four main production processes of the Standard Model Higgs boson at the LHC and the Tevatron. In the Higgs mass range where the production rates are large enough, the spread in the uncertainties when the three sets of PDFs are compared is of about 15% in all processes and at both colliders. However, within one given set of PDFs, the deviations from the values obtained with the reference sets are much smaller, being of %), except in the gluon--gluon fusion mechanism at relatively large Higgs boson masses, where they can reach the level of 10% (15%) at the LHC (Tevatron).Using the new schemes provided by CTEQ and MRST Collaborations and by Alekhin, we analyze the uncertainties due to the parton distribution functions (PDFs) on the next-to-leading-order cross sections of the four main production processes of the Standard Model Higgs boson at the LHC and the Tevatron. In the Higgs mass range where the production rates are large enough, the spread in the uncertainties when the three sets of PDFs are compared is of about 15% in all processes and at both colliders. However, within one given set of PDFs, the deviations from the values obtained with the reference sets are much smaller, being of O (5%), except in the gluon–gluon fusion mechanism at relatively large Higgs boson masses, where they can reach the level of 10% (15%) at the LHC (Tevatron)
Synthesis of a Polyacrylamide Hydrogel using CO2 at Room Temperature
No full textCarbon dioxide (CO2) is an environmentally harmful “greenhouse gas” that is present in abundant quantities in the earth’s atmosphere. Thus, the sequestration and conversion of CO2 to value-added organic chemicals is of environmental and economical importance. In this proof-of-concept study, amine groups of acrylamide compounds were found to react with CO2 under ambient conditions to form a polyacrylamide hydrogel. This composite was characterized using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR) and electrospray ionization mass spectrometry (ESI–MS), which confirmed successful synthesis and demonstrated all characteristics representative of a typical hydrogel material. Rheology analyses further proved the formation of the hydrogel, as well as its self-healing nature. The novel approach proposed in this work can potentially be used in the construction of versatile amine-based gel materials for efficient CO2 utilization applications.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
Insights into the physicochemical properties of Sugar Scum as a sustainable biosorbent derived from sugar refinery waste for efficient cationic dye removal
No full textThe objective of this study was to determine the ability of sugar scum (SS), an industrial waste, as a novel biosorbent for the removal of Basic Blue 41 (BB 41) from aqueous solutions. The biosorbent was characterized by SEM/EDS, BET, FTIR, and pHpzc measurements, respectively. To reach a maximum adsorption capacity of 26.45 mg.g–1, impacting operational factors such as pH, biosorbent dose, contact duration, starting dye concentration, and temperature were adjusted, when the removal efficiency reached 84% during 60 min at pH 10, 1.5 g.L–1 of biosorbent and Co = 10 mg.L–1. The experimental data were modeled by various isotherm models, whereas the best fit was found for Freundlich with a high correlation coefficient (R 2 = 0.991). Other kinetic models including pseudo-first, pseudo-second order, and intra-particle diffusion models were tested to fit the kinetic data. The biosorption of BB 41 onto SS was spontaneous (∆G° < 0) and exothermic (∆H° < 0), while the biosoprtion mechanism of BB41 over SS was proposed with repeated reuse showing that SS could be regenerated after four successive runs. Furthermore, this study revealed that sugar scum is an underutilized bioresource in Algeria, with the potential to provide low-cost environmental removal of additional contaminants in the wastewater treatment domain. Graphical abstract: [Figure not available: see fulltext.] © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.The authors are grateful to Dr. F. Ferrag-Siagh, M. C. Nebbar, and M. Kebir for characterization measurements.
This work was financially supported by the Mechanical Engineering and Engineering Process (USTHB,Algiers).Peer reviewe
A measurement of the charged and neutral B meson lifetimes using fully reconstructed decays
No full textThe author first notes that Google, the trade mark, the project, the utilization - the "googling"- are social facts proven by the numbers – number of net surfers, of requests, of uses- and by the signs of adhesion - linguistics, economic, social. A socio linguistic analysis of the speeches of the persons in charge of Google and of users indicate that the social fact "googling" results in the emergence of a culture and a world community which shares it. They are supported by the language and also by the myths that were created and largely maintained by the owners the mark "Google Inc". The conclusion is that the current organization of the market of services on the Internet makes that Google Inc. is almost the only institution to know the population of the googlers. In order not to be subjected to this monopoly, however comfortable it is, the author proposes to develop a research program on the uses and users of Google
A measurement of the charged and neutral B meson lifetimes using fully reconstructed decays
No full textThe author first notes that Google, the trade mark, the project, the utilization - the "googling"- are social facts proven by the numbers – number of net surfers, of requests, of uses- and by the signs of adhesion - linguistics, economic, social. A socio linguistic analysis of the speeches of the persons in charge of Google and of users indicate that the social fact "googling" results in the emergence of a culture and a world community which shares it. They are supported by the language and also by the myths that were created and largely maintained by the owners the mark "Google Inc". The conclusion is that the current organization of the market of services on the Internet makes that Google Inc. is almost the only institution to know the population of the googlers. In order not to be subjected to this monopoly, however comfortable it is, the author proposes to develop a research program on the uses and users of Google
Search for dark matter candidates and large extra dimensions in events with a jet and missing transverse momentum with the ATLAS detector
No full textOpen Access, Copyright CERN, for the benefit of the ATLAS collaboration. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited
Evidence for Electroweak Production of W(+/-)W(+/-)jj in pp Collisions at root s=8 TeV with the ATLAS Detector
No full textFurther distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.This Letter presents the first study of W±W±jj, same-electric-charge diboson production in association with two jets, using 20.3 fb-1 of proton-proton collision data at √s=8 TeV recorded by the ATLAS detector at the Large Hadron Collider. Events with two reconstructed same-charge leptons (e±e±, e±μ±, and μ±μ±) and two or more jets are analyzed. Production cross sections are measured in two fiducial regions, with different sensitivities to the electroweak and strong production mechanisms. First evidence for W±W±jj production and electroweak-only W±W±jj production is observed with a significance of 4.5 and 3.6 standard deviations, respectively. The measured production cross sections are in agreement with standard model predictions. Limits at 95% confidence level are set on anomalous quartic gauge couplings
Network traffic datasets with novel extended IP flow called NetTiSA flow
No full text<p><strong>Network traffic datasets with novel extended IP flow called NetTiSA flow</strong></p>
<p>Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka -- which is published in The International Journal of Computer and Telecommunications Networking <a href="https://doi.org/10.1016/j.comnet.2023.110147" rel="nofollow">https://doi.org/10.1016/j.comnet.2023.110147</a><br><br>Please cite the usage of our datasets as:</p>
<blockquote>
<p>Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286<br><br></p>
<pre><code>@article{KOUMAR2024110147,
title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification},
journal = {Computer Networks},
volume = {240},
pages = {110147},
year = {2024},
issn = {1389-1286},
doi = {https://doi.org/10.1016/j.comnet.2023.110147},
url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923},
author = {Josef Koumar and Karel Hynek and Jaroslav Pešek and Tomáš Čejka}
}
</code></pre>
</blockquote>
<p>This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.<br><br> </p>
<p><strong>NetTiSA flow feature vector</strong></p>
<p><br>The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets. The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.</p>
<p> </p>
<p><strong>Flow features</strong></p>
<p>The flow features are:</p>
<ul>
<li><strong><em>Packets</em></strong> is the number of packets in the direction from the source to the destination IP address.</li>
<li><em><strong>Packets in reverse order</strong></em> is the number of packets in the direction from the destination to the source IP address.</li>
<li><strong><em>Bytes</em> </strong>is the size of the payload in bytes transferred in the direction from the source to the destination IP address.</li>
<li><strong><em>Bytes in reverse order</em></strong> is the size of the payload in bytes transferred in the direction from the destination to the source IP address.</li>
</ul>
<p> </p>
<p><strong>Statistical and Time-based features</strong></p>
<p>The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features:</p>
<ul>
<li><strong><em>Mean</em></strong> represents mean of the payload lengths of packets</li>
<li><strong><em>Min</em></strong> is the minimal value from payload lengths of all packets in a flow</li>
<li><strong><em>Max</em></strong> is the maximum value from payload lengths of all packets in a flow</li>
<li><strong><em>Standard deviation</em></strong> is a measure of the variation of payload lengths from the mean payload length</li>
<li><strong><em>Root mean square</em></strong> is the measure of the magnitude of payload lengths of packets</li>
<li><strong><em>Average dispersion</em></strong> is the average absolute difference between each payload length of the packet and the mean value</li>
<li><strong><em>Kurtosis</em></strong> is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution</li>
<li><em><strong>Mean of relative times</strong></em> is the mean of the relative times which is a sequence defined as <span></span></li>
<li><em><strong>Mean of time differences</strong></em> is the mean of the time differences which is a sequence defined as <span></span></li>
<li><em><strong>Min from time differences</strong></em> is the minimal value from all time differences, i.e., min space between packets.</li>
<li><em><strong>Max from time differences</strong></em> is the maximum value from all time differences, i.e., max space between packets.</li>
<li><em><strong>Time distribution</strong></em> describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation:<br><span></span></li>
<li><em><strong>Switching ratio</strong></em> represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation:<br><span></span></li>
</ul>
<p> where <span></span> is number of switches.</p>
<p> </p>
<p><strong>Features computed at the collector</strong><br>The third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features:</p>
<ul>
<li><em><strong>Max minus min</strong></em> is the difference between minimum and maximum payload lengths</li>
<li><em><strong>Percent deviation</strong></em> is the dispersion of the average absolute difference to the mean value</li>
<li><em><strong>Variance</strong></em> is the spread measure of the data from its mean</li>
<li><em><strong>Burstiness</strong></em> is the degree of peakedness in the central part of the distribution</li>
<li><em><strong>Coefficient of variation</strong></em> is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement</li>
<li><em><strong>Directions</strong></em> describe a percentage ratio of packet direction computed as <span></span>, where <span></span> is a number of packets in a direction from source to destination IP address and <span></span> the opposite direction. Both <span></span> and <span></span> are inside the classical bidirectional flow.</li>
<li><em><strong>Duration</strong></em> is the duration of the flow</li>
</ul>
<p> </p>
<p>The NetTiSA flow is implemented into IP flow exporter <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a>.</p>
<p> </p>
<p><strong>Description of dataset files</strong></p>
<p>In the following table is a description of each dataset file:</p>
<table>
<tbody>
<tr>
<td>
<p><strong>File name</strong></p>
</td>
<td>
<p><strong>Detection problem</strong></p>
</td>
<td>
<p><strong>Citation of the original raw dataset</strong></p>
</td>
</tr>
<tr>
<td>botnet_binary.csv </td>
<td>Binary detection of botnet </td>
<td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td>
</tr>
<tr>
<td>botnet_multiclass.csv </td>
<td>Multi-class classification of botnet </td>
<td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td>
</tr>
<tr>
<td>cryptomining_design.csv </td>
<td>Binary detection of cryptomining; the design part </td>
<td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td>
</tr>
<tr>
<td>cryptomining_evaluation.csv </td>
<td>Binary detection of cryptomining; the evaluation part </td>
<td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td>
</tr>
<tr>
<td>dns_malware.csv </td>
<td>Binary detection of malware DNS </td>
<td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td>
</tr>
<tr>
<td>doh_cic.csv </td>
<td>Binary detection of DoH </td>
<td>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </td>
</tr>
<tr>
<td>doh_real_world.csv </td>
<td>Binary detection of DoH </td>
<td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td>
</tr>
<tr>
<td>dos.csv </td>
<td>Binary detection of DoS </td>
<td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. </td>
</tr>
<tr>
<td>edge_iiot_binary.csv </td>
<td>Binary detection of IoT malware </td>
<td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td>
</tr>
<tr>
<td>edge_iiot_multiclass.csv </td>
<td>Multi-class classification of IoT malware </td>
<td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td>
</tr>
<tr>
<td>https_brute_force.csv </td>
<td>Binary detection of HTTPS Brute Force </td>
<td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 </td>
</tr>
<tr>
<td>ids_cic_binary.csv </td>
<td>Binary detection of intrusion in IDS </td>
<td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td>
</tr>
<tr>
<td>ids_cic_multiclass.csv </td>
<td>Multi-class classification of intrusion in IDS </td>
<td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td>
</tr>
<tr>
<td>unsw_binary.csv </td>
<td>Binary detection of intrusion in IDS </td>
<td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td>
</tr>
<tr>
<td>unsw_multiclass.csv </td>
<td>Multi-class classification of intrusion in IDS </td>
<td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td>
</tr>
<tr>
<td>iot_23.csv </td>
<td>Binary detection of IoT malware </td>
<td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 </td>
</tr>
<tr>
<td>ton_iot_binary.csv </td>
<td>Binary detection of IoT malware </td>
<td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td>
</tr>
<tr>
<td>ton_iot_multiclass.csv </td>
<td>Multi-class classification of IoT malware </td>
<td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td>
</tr>
<tr>
<td>tor_binary.csv </td>
<td>Binary detection of TOR </td>
<td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td>
</tr>
<tr>
<td>tor_multiclass.csv </td>
<td>Multi-class classification of TOR </td>
<td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td>
</tr>
<tr>
<td>vpn_iscx_binary.csv </td>
<td>Binary detection of VPN </td>
<td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td>
</tr>
<tr>
<td>vpn_iscx_multiclass.csv </td>
<td>Multi-class classification of VPN </td>
<td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td>
</tr>
<tr>
<td>vpn_vnat_binary.csv </td>
<td>Binary detection of VPN </td>
<td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td>
</tr>
<tr>
<td>vpn_vnat_multiclass.csv </td>
<td>Multi-class classification of VPN </td>
<td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td>
</tr>
</tbody>
</table>
<p> </p>
<p> </p>
<p> </p>
<p> </p>This research was funded by the Ministry of Interior of the Czech Republic, grant No. VJ02010024: Flow-Based Encrypted Traffic Analysis and also by the Grant Agency of the CTU in Prague, grant No. SGS23/207/OHK3/3T/18 funded by the MEYS of the Czech Republic
