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    <responseDate>2026-10-15T22:45:17Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/f6sknjshzy.1</identifier>
                <datestamp>2023-11-29T16:45:30Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Roldán-Gómez, José</dc:creator>
    <dc:title>An Automatic Complex Event Processing Rules Generation System for the Recognition of Real-Time IoT Attack Patterns (Dataset)</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>The dataset contains data from different attacks on an IoT network. In addition to data on the legitimate traffic of the network. You can also find the CEP rules used to detect attacks.

This dataset has been used in the work : &quot;An Automatic Complex Event Processing Rules Generation System for the Recognition of Real-Time IoT Attack Patterns&quot;.


The distribution between training and testing is as follows:

Normal: 20% training, 80% testing
Subscription fuzzing: 20% training, 80% testing
Disconnection wave: 20%training, 80% testing
TCP SYN Scan: 70% training, 30% testing
UDP port Scan: 70% training, 30% testing
XMAS Scan: 90% training, 10% testing
Telnet Connection: 70% training, 30% testing</dc:description>
    <dc:subject>Security</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/f6sknjshzy.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/f6sknjshzy.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/f6sknjshzy</dc:relation>
    <dc:date>2023-11-29T16:45:30Z</dc:date>
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