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  <titleInfo>
    <title>Raspberry PI-based investigation model for identifying and collecting intrusion evidences</title>
  </titleInfo>
  <name type="personal">
    <namePart>Yee, Wai Kit</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <typeOfResource manuscript="yes">text</typeOfResource>
  <genre authority="marc">abstract or summary</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">my</placeTerm>
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    <place>
      <placeTerm type="text">Kuantan, Pahang</placeTerm>
    </place>
    <publisher>UMP</publisher>
    <dateIssued>2016</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
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    <extent>xv, 65 p. : ill. (some col.) ; 30 cm. + 1 CD-ROM</extent>
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  <abstract>Nowadays it is very important to maintain an intermediary level of security to ensure safe and trusted communication of information for daily usage. However, a secured data communication over internet and any other network is hard to achieve due to the threat of intrusions and misuses. None of the existing systems proved to be flawless, though various approaches being utilised for thwarting network intrusion activities, such as Denial of Service (DoS) attack, Man-in-the Middle attack and also IP address spoofing. This research proposed a system for Identifying and Collecting Intrusion Evidences (MICIE) in order to study about intrusion behaviour based on collected evidences. In particular, the proposed system MICIE comprises three main features, SNORT as IDS, MySQL as database and BASE for result viewing. These features were installed on Raspberry Pi, which was used to aid the data collection process. For the course of this research, Modified Waterfall Model is used for keeping track of the progress and to ensure that the task delegation can be fulfilled before meeting deadlines. The results demonstrated that the proposed system is promising for identifying and collecting evidence of network intrusions in real time.</abstract>
  <targetAudience authority="marctarget">specialized</targetAudience>
  <note type="statement of responsibility">Yee Wai Kit</note>
  <note>Faculty of Computer Systems and Software Engineering</note>
  <note>Project paper (Bachelor of Computer Science (Computer Systems &amp; Networking) With Honours) -- Universiti Malaysia Pahang – 2016</note>
  <note>Bibliography : p. 64-65</note>
  <subject authority="lcsh">
    <name type="corporate">
      <namePart>Faculty of Computer Systems and Software Engineering</namePart>
    </name>
    <topic>Dissertations</topic>
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  <subject authority="lcsh">
    <topic>Universities and Colleges</topic>
    <topic>Dissertations</topic>
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  <subject authority="lcsh">
    <topic>Theses</topic>
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  <identifier type="isbn">THE0001289(Local)</identifier>
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    <recordCreationDate encoding="marc">170420</recordCreationDate>
    <recordChangeDate encoding="iso8601">20251117113326.0</recordChangeDate>
    <recordIdentifier source="KUKTEM">vtls000099242</recordIdentifier>
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