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    <title>GI Sicherheit on Insinuator.net - Bold Statements</title>
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      <title>GI Sicherheit 2018 Conference</title>
      <link>https://insinuator.net/2018/05/gi-sicherheit-2018-conference/</link>
      <pubDate>Thu, 03 May 2018 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;Last week (25^(th) – 27^(th) April), I attended the “Sicherheit 2018” in Konstanz which is the annual meeting of the security community of the Gesellschaft für Informatik e.V. (GI) in Germany. The conference is in equal proportions attended by researchers and people of the industry working in security-related disciplines which lead to lively and pleasant discussions conversations.&lt;/p&gt;&#xA;&lt;p&gt;The topics discussed were contentual wide-reaching, so there were very technical talks like Sebastian Banescu who was the winner and one of two candidates nominated for the best PhD thesis award presenting about “Characterizing the Strength of Software Obfuscation Against Automated Attacks”, as well as conceptual presentations such as Sabrina Krausz elucidated her bachelor thesis about an integrated procedure model for planning and implementing an ISMS on the example of the pharmaceutical production.&lt;/p&gt;</description>
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      <title>Summary GI Sicherheit</title>
      <link>https://insinuator.net/2016/04/summary-gi-sicherheit/</link>
      <pubDate>Fri, 08 Apr 2016 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;This is a short summary of selected talks (i.e. those that I found the most interesting of those I was able to personally attend) of the &lt;em&gt;&lt;a href=&#34;https://sicherheit2016.de/&#34;&gt;GI Sicherheit 2016&lt;/a&gt;&lt;/em&gt;.&lt;/p&gt;&#xA;&lt;p&gt;First of all, congratulations to Dr. Fabian Yamaguchi, who received an award (the &lt;em&gt;GI Promotionspreis&lt;/em&gt;) for his PhD thesis “&lt;a href=&#34;https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-9682-0/mainFastWeb.pdf&#34;&gt;Pattern-Based Vulnerability Discovery&lt;/a&gt;“!&lt;br&gt;&#xA;His work presents an “approach for identifying vulnerabilities which combines techniques from static analysis, machine learning, and graph mining to augment the analyst’s abilities rather than trying to replace her” by identifying and highlighting patterns of potential vulnerabilities in source code.&lt;/p&gt;</description>
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