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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=generative+adversarial+network]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for generative adversarial network]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1179/KiNETGAN-Enabling-Distributed-Network-Intrusion-Detection-through-Knowledge-Infused-Synthetic-Data-Generation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1136/Knowledge-Infusion-in-Privacy-Preserving-Data-Generation"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/485/Generative-Adversarial-Networks-An-Introduction">
  <title><![CDATA[Generative Adversarial Networks, An Introduction]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/485/Generative-Adversarial-Networks-An-Introduction</link>
  <description><![CDATA[While deep learning has made historic improvements in speech recognition and object recognition in recent years, almost all of these gains have been in supervised learning of now fairly well understood discriminative models. In the larger context of machine learning, less is understood about both unsupervised and generative models, but Generative Adversarial Networks have emerged as a promising approach to making progress in that direction. 

We are going to introduce Generative Adversarial...]]></description>
  <dc:date>2017-02-01</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1179/KiNETGAN-Enabling-Distributed-Network-Intrusion-Detection-through-Knowledge-Infused-Synthetic-Data-Generation">
  <title><![CDATA[KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1179/KiNETGAN-Enabling-Distributed-Network-Intrusion-Detection-through-Knowledge-Infused-Synthetic-Data-Generation</link>
  <description><![CDATA[In the realm of IoT/CPS systems connected over mobile networks, traditional intrusion detection methods analyze network traffic across multiple devices using anomaly detection techniques to flag potential security threats. However, these methods face significant privacy challenges, particularly with deep packet inspection and network communication analysis. This type of monitoring is highly intrusive, as it involves examining the content of data packets, which can include personal and sensiti...]]></description>
  <dc:date>2024-05-26</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1136/Knowledge-Infusion-in-Privacy-Preserving-Data-Generation">
  <title><![CDATA[Knowledge Infusion in Privacy Preserving Data Generation]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1136/Knowledge-Infusion-in-Privacy-Preserving-Data-Generation</link>
  <description><![CDATA[Security monitoring is crucial for maintaining a strong IT infrastructure by protecting against emerging threats, identifying vulnerabilities, and detecting potential points of failure. It involves deploying advanced tools to continuously monitor networks, systems, and configurations. However, organizations face challenges in adapting modern techniques like Machine Learning (ML) due to privacy and security risks associated with sharing internal data.  Compliance with regulations like GDPR fur...]]></description>
  <dc:date>2023-08-06</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1014/PriveTAB-Secure-and-Privacy-Preserving-sharing-of-Tabular-Data">
  <title><![CDATA[PriveTAB : Secure and Privacy-Preserving sharing of Tabular Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1014/PriveTAB-Secure-and-Privacy-Preserving-sharing-of-Tabular-Data</link>
  <description><![CDATA[Machine Learning has increased our ability to model large quantities of data efficiently in a short time. Machine learning approaches in many application domains require collecting large volumes of data from distributed sources and combining them. However, sharing of data from multiple sources leads to concerns about privacy. Privacy regulations like European Union's General Data Protection Regulation (GDPR) have specific requirements on when and how such data can be shared. Even when there a...]]></description>
  <dc:date>2022-04-24</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/898/NAttack-Adversarial-Attacks-to-bypass-a-GAN-based-classifier-trained-to-detect-Network-intrusion">
  <title><![CDATA[NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/898/NAttack-Adversarial-Attacks-to-bypass-a-GAN-based-classifier-trained-to-detect-Network-intrusion</link>
  <description><![CDATA[With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attack were detected using well-crafted rules. An attacker who has knowledge in the field of cyber-defense could make educated guesses to sometimes accurately predict which particular features of network traffic data the cyber-defense mechanism is looking at. Wit...]]></description>
  <dc:date>2020-05-26</dc:date>
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