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I downloaded a dataset for intrusion detection. It's from the honeypot systems of Kyoto University (2013 dataset). I'll be using the dataset for training a neural network. My problem is how to process the dataset. Am I going to use vector space model or what?

A sample from the dataset:

0.000000,other,0,0,0,0.00,0.00,0.00,1,37,1.00,0.00,0.00,RSTOS0,0,0,0,1,fd75:41fb:cf76:971a:0b40:03a2:4ab7:0f81,38581,fd75:41fb:cf76:dc4c:7d2c:2705:07b2:0f45,25,00:00:00,tcp

The specifications of the features are as follows (further details):

  1. Duration
  2. Service
  3. Source bytes
  4. Destination bytes
  5. Count
  6. Same_srv_rate
  7. Serror_rate
  8. Srv_serror_rate
  9. Dst_host_count
  10. Dst_host_srv_count
  11. Dst_host_same_src_port_rate
  12. Dst_host_serror_rate
  13. Dst_host_srv_serror_rate
  14. Flag
  15. IDS_detection
  16. Malware_detection
  17. Ashula_detection
  18. Label
  19. Source_IP_Address
  20. Source_Port_Number
  21. Destination_IP_Address
  22. Destination_Port_Number
  23. Start_Time
  24. Duration
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  • $\begingroup$ If it is a publicly available dataset, i doubt that you are the first one to work on it. I recommend to read papers on how others preprocessed the same dataset $\endgroup$ Jul 10, 2017 at 9:18
  • $\begingroup$ Thanks, @NikolasRieble I did read one paper, the other night. What they did was to index the symbolic features like service to [0, n-1] where n is the number of symbols. As for the integer values, they did a linear scaling to [0.0, 1.0]. What I am now having a trouble with is how they did the linear scaling. $\endgroup$
    – afagarap
    Jul 11, 2017 at 5:02
  • $\begingroup$ If I understand correctly, a linear scaling here means first $x_i = x_i - min_i(x_i)$ and the $x_i = x_i / max_i(x_i)$ for all values. $\endgroup$ Jul 11, 2017 at 9:15
  • $\begingroup$ I actually defined it as a function $f(x) = \frac{(b - a) \times (x - min)}{(max - min)} + a$ where $[min, max] \rightarrow [a, b]$. $\endgroup$
    – afagarap
    Jul 11, 2017 at 9:51
  • $\begingroup$ $[a, b]$ is the scale range, for my problem, it's $[0.0, 1.0]$. $\endgroup$
    – afagarap
    Jul 11, 2017 at 9:52

1 Answer 1

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For the categorical data like service and flag, I'll be indexing them to $[0, n-1]$ where $n$ is the number of categories in the feature.

As for the numerical data, I'll be standardizing them using the Student's t-statistic $z = \dfrac{x - \mu}{\sigma}$.

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