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TCP FIN Flood and Zbassocflood Dataset

Stiawan, Deris; Wahyudi, Dimas; Heryanto, Ahmad; Septian, Tri Wanda; Wahyudi, Johan; Andika, Riki; Suryani, Meilinda Eka


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  <identifier identifierType="DOI">10.5281/zenodo.4431541</identifier>
  <creators>
    <creator>
      <creatorName>Stiawan, Deris</creatorName>
      <givenName>Deris</givenName>
      <familyName>Stiawan</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
    <creator>
      <creatorName>Wahyudi, Dimas</creatorName>
      <givenName>Dimas</givenName>
      <familyName>Wahyudi</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
    <creator>
      <creatorName>Heryanto, Ahmad</creatorName>
      <givenName>Ahmad</givenName>
      <familyName>Heryanto</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
    <creator>
      <creatorName>Septian, Tri Wanda</creatorName>
      <givenName>Tri Wanda</givenName>
      <familyName>Septian</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
    <creator>
      <creatorName>Wahyudi, Johan</creatorName>
      <givenName>Johan</givenName>
      <familyName>Wahyudi</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
    <creator>
      <creatorName>Andika, Riki</creatorName>
      <givenName>Riki</givenName>
      <familyName>Andika</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
    <creator>
      <creatorName>Suryani, Meilinda Eka</creatorName>
      <givenName>Meilinda Eka</givenName>
      <familyName>Suryani</familyName>
      <affiliation>Universitas Sriwijaya</affiliation>
    </creator>
  </creators>
  <titles>
    <title>TCP FIN Flood and Zbassocflood Dataset</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Internet of things</subject>
    <subject>IoT Dataset</subject>
    <subject>zbassofflood dataset</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-12-11</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4431541</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4431540</relatedIdentifier>
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  <version>1</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Abstract&amp;mdash; This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.&lt;/p&gt;

&lt;p&gt;A minimal requirements Internet of Things (IoT) network system was built to produce a dataset according to IDS testing needs for IoT security. Testing was performed with 12 scenarios and resulted in 24 datasets which consisted of normal, attack and combined normal-attack traffic data. Testing focused on three denial of service (DoS) and distributed denial of service (DDoS) attacks&amp;mdash;&amp;ldquo;finish&amp;rdquo; (FIN) flood, User Datagram Protocol (UDP) flood, and Zbassocflood/association flood&amp;mdash;using two communication protocols, IEEE 802.11 (WiFi) and IEEE 802.15.4 (ZigBee). A preprocessing test result obtained 95 attributes for the WiFi datasets and 64 attributes for the Xbee datasets .&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Abstract-Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenarios normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.&lt;/p&gt;

&lt;p&gt;Citing&lt;br&gt;
Citation data : &amp;quot;TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis&amp;quot; - &lt;a href="https://online-journals.org/index.php/i-joe/article/view/9848"&gt;https://online-journals.org/index.php/i-joe/article/view/9848&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@article{article,

author = {Stiawan, Deris and Wahyudi, Dimas and Heryanto, Ahmad and Sahmin, Samsuryadi and Idris, Yazid and Muchtar, Farkhana and Alzahrani, Mohammed and Budiarto, Rahmat},

year = {2019},
month = {04},
pages = {124},
title = {TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis},
volume = {15},
journal = {International Journal of Online and Biomedical Engineering (iJOE)},
doi = {10.3991/ijoe.v15i07.9848}
}&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent.&lt;/p&gt;

&lt;p&gt;Citing&lt;br&gt;
Citation data : &amp;quot;Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)&amp;quot; - &lt;a href="https://ieeexplore.ieee.org/document/9251292"&gt;https://ieeexplore.ieee.org/document/9251292&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@inproceedings{inproceedings,

author = {Sharipuddin, and Purnama, Benni and Kurniabudi, Kurniabudi and Winanto, Eko and Stiawan, Deris and Hanapi, Darmawiiovo and Idris, Mohd and Budiarto, Rahmat},

year = {2020},
month = {10},
pages = {114-118},
title = {Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)},
doi = {10.23919/EECSI50503.2020.9251292}
}&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
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