Answers.Body: <p>This seems to fall broadly into the regime of a <a href="https://en.wikipedia.org/wiki/Statistical_classification" rel="nofollow">classification problem</a> as you want to classify an outgoing communication as "contains proprietary information" or "does not contain proprietary information".  As such, any classification approach could be applied.  Neural Networks certainly seem like a valid approach, but you might also get good mileage out of Random Forests, Support Vector Machines, a Naive Bayes classifier, etc.</p>
  
  <p>GA's are more aimed towards optimization than classification, so I wouldn't say that a GA, in and of itself, would map cleanly to solving this kind of problem. If GA's had applicability here, I think it would be more likely to be in terms of training a model rooted in one of the other techniques.     </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-14T05:57:55.947Z
Answers.LastActivityDate: 2016-08-14T05:57:55.947Z
Answers.UserDisplayName: mindcrime
Body: <p>I'm investigating applications of AI algorithms which can be used for data leakage detection and prevention within an intranet network (like <a href="https://en.wikipedia.org/wiki/Forcepoint" rel="nofollow">Forcepoint</a>). More specifically detecting traffic patterns. I'm new to this.</p>
  
  <p>Which learning algorithms are most suitable for this goal? <a href="https://en.wikipedia.org/wiki/Evolutionary_algorithm" rel="nofollow">EA</a>, <a href="https://en.wikipedia.org/wiki/Genetic_algorithm" rel="nofollow">GA</a>, <a href="https://en.wikipedia.org/wiki/Artificial_neural_network" rel="nofollow">ANN</a> (which one) or something else?</p>
  
Tags: <learning-algorithms>
Title: Which learning algorithms are suitable for data leakage detection and prevention?
UserDisplayName: kenorb
ThreadText: # Which learning algorithms are suitable for data leakage detection and prevention?
  
  (Asked by: username_0 on 2016-08-13)
  
  I'm investigating applications of AI algorithms which can be used for data leakage detection and prevention within an intranet network (like [Forcepoint](https://en.wikipedia.org/wiki/Forcepoint)). More specifically detecting traffic patterns. I'm new to this.
  
  Which learning algorithms are most suitable for this goal? [EA](https://en.wikipedia.org/wiki/Evolutionary_algorithm), [GA](https://en.wikipedia.org/wiki/Genetic_algorithm), [ANN](https://en.wikipedia.org/wiki/Artificial_neural_network) (which one) or something else?
  
  **Tags:** learning-algorithms
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2016-08-14. Score: 3 | Accepted answer
  
  This seems to fall broadly into the regime of a [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) as you want to classify an outgoing communication as "contains proprietary information" or "does not contain proprietary information". As such, any classification approach could be applied. Neural Networks certainly seem like a valid approach, but you might also get good mileage out of Random Forests, Support Vector Machines, a Naive Bayes classifier, etc.
  
  GA's are more aimed towards optimization than classification, so I wouldn't say that a GA, in and of itself, would map cleanly to solving this kind of problem. If GA's had applicability here, I think it would be more likely to be in terms of training a model rooted in one of the other techniques.
  
  
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