<?xml version="1.0" encoding="UTF-8"?>
<mods xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.loc.gov/mods/v3" version="3.1" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
  <titleInfo>
    <title>Implementation of Security Systems for Detection and Prevention of Data Loss/Leakage at Organization via Traffic Inspection</title>
  </titleInfo>
  <name type="personal">
    <namePart>Mir Hassan</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">xx</placeTerm>
    </place>
    <dateIssued encoding="marc">2020</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">ng </languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
  </physicalDescription>
  <abstract>Data Loss/Leakage Prevention (DLP) continues to be the main issue for many large organizations. There are multiple numbers of emerging security attach scenarios and a limitless number of overcoming solutions. Today's enterprises' major concern is to protect confidential information because a leakage that compromises confidential data means that sensitive information is in competitors' hands. Different data types need to be protected. However, our research is focused only on data in motion (DIM) i-e data transferred through the network. The research and scenarios in this paper demonstrate a recent survey on information and data leakage incidents, which reveals its importance and also proposed a model solution that will offer the combination of previous methodologies with a new way of pattern matching by advanced content checker based on the use of machine learning to protect data within a</abstract>
  <note>Open access — freely available to read.</note>
  <identifier type="uri">https://arxiv.org/pdf/2012.14111v1</identifier>
  <location>
    <url displayLabel="Read the full paper (PDF)">https://arxiv.org/pdf/2012.14111v1</url>
  </location>
  <accessCondition type="restrictionOnAccess">Open access — freely available to read.</accessCondition>
  <recordInfo>
    <recordCreationDate encoding="marc">250101</recordCreationDate>
    <recordChangeDate encoding="iso8601">20260901030331.0</recordChangeDate>
  </recordInfo>
</mods>
