Purdue University Graduate School
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Generation of cyber attack data using generative techniques

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posted on 2019-05-15, 16:32 authored by Nidhi Nandkishor SakhalaNidhi Nandkishor Sakhala

The presence of attacks in day-to-day traffic flow in connected networks is considerably less compared to genuine traffic flow. Yet, the consequences of these attacks are disastrous. It is very important to identify if the network is being attacked and block these attempts to protect the network system. Failure to block these attacks can lead to loss of confidential information and reputation and can also lead to financial loss. One of the strategies to identify these attacks is to use machine learning algorithms that learn to identify attacks by looking at previous examples. But since the number of attacks is small, it is difficult to train these machine learning algorithms. This study aims to use generative techniques to create new attack samples that can be used to train the machine learning based intrusion detection systems to identify more attacks. Two metrics are used to verify that the training has improved and a binary classifier is used to perform a two-sample test for verifying the generated attacks.


Degree Type

  • Master of Science


  • Computer and Information Technology

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. John Springer

Additional Committee Member 2

Dr. Julia Taylor Rayz

Additional Committee Member 3

Dr. J. Eric Dietz