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    <title>DSpace Collection:</title>
    <link>http://localhost:8080/xmlui/handle/123456789/2140</link>
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    <pubDate>Tue, 23 Jun 2026 06:13:04 GMT</pubDate>
    <dc:date>2026-06-23T06:13:04Z</dc:date>
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      <title>Effective Intrusion Detection Systems using Genetic Algorithm</title>
      <link>http://localhost:8080/xmlui/handle/123456789/2141</link>
      <description>Title: Effective Intrusion Detection Systems using Genetic Algorithm
Authors: Kalavadekar, Mr. Prakash N; Sane, Shirish S
Abstract: Conventional methods of intrusion prevention like&#xD;
firewalls, cryptography techniques, have not proved themselves to&#xD;
completely defend networks and systems from newly generated&#xD;
malwares and attacks. Intrusion Detection Systems (IDS) are&#xD;
useful to find the correct solution to solve the current problems&#xD;
and became an important part of any security network&#xD;
infrastructure to detect these threats without generating any&#xD;
problem to network. The basic purpose of IDS is to detect attacks&#xD;
and their nature that may harm the computer system. Several&#xD;
different approaches for intrusion detection are available as per&#xD;
the literature. These approaches are broadly defined by three&#xD;
ways: i) Signature based approach ii) Anomaly based approach&#xD;
and iii) Hybrid approach that combines signature and anomaly&#xD;
detection approaches. The proposed system works for signature&#xD;
based concept using genetic algorithm as features selection and&#xD;
detection .The system is tested on KDDCup99 and NSL-KDD&#xD;
dataset using Weka3.6 classifiers and implemented classifier.</description>
      <pubDate>Wed, 01 Mar 2017 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://localhost:8080/xmlui/handle/123456789/2141</guid>
      <dc:date>2017-03-01T00:00:00Z</dc:date>
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