Malicious URL example

Detecting Malicious URLs

The malicious URL dataset from UCSD represents a sequential binary classification problem. The data are temporally correlated, and thus the problem is particularly suitable for online learning approaches like VW. Here we show how to evaluate the "out-of-the-box" performance of VW on this task.

Preparing the data

First, download the data in SVM-light format and extract the files from the tar-ball. tar xzf url_svmlight.tar.gz

The following command-line converts these data from SVM-light format to VW input format:

for d in `seq 0 120`; do cat url_svmlight/Day$d.svm; done | \
  sed -e 's/^-1/0 |f/' |sed -e 's/^+1/1 |f/' |sed -e 's/$/ const:.01/'

This conversion accomplishes the following:

• Converts the labels from "-1" to "0" and from "+1" to "1"
• Puts the features into a namespace called "f"
• Adds a constant feature called "const" with value ".01".
• Retains the temporal order of the data.

This shell pipeline can be used directly with VW, as described below.

Out-of-the-box online training and testing

We can use the above command-line to feed the data directly into VW to simulate online training and testing: time for d in seq 0 120; do cat url_svmlight/Day$d.svm; done
|sed -e 's/^-1/0 |f/' |sed -e 's/^+1/1 |f/' |sed -e 's/$/ const:.01/'
|vw --adaptive --cache_file cache

The command line arguments used above are:

• --adaptive: use per-feature adaptive learning rates; this is sensible for highly diverse and variable features
• --cache_file cache: cache the parsed input data into the file cache

It also uses time to measure the approximate wall-clock execution time.

Results

The output of the above command-line concludes with the following.

finished run
number of examples = 2396130
weighted example sum = 2.396e+06
weighted label sum = 7.921e+05
average loss = 0.0127
best constant = 0.3306
best constant's loss = 0.2213
total feature number = 281850904

real    3m28.111s
user    2m36.850s
sys     0m17.050s

The average square loss over all 2396130 examples is 0.0127. The wall-clock execution time is 3 minutes 28 seconds. This may alarm you (or not), but most of time is spent parsing. If you re-run the command-line, it will read the cached data from cache and give the same result, except for the execution time:

real    0m17.982s
user    0m20.650s
sys     0m9.340s

Evaluating prediction accuracy

If you want to compare the actual predictions to the true labels, re-run the command-line with the additional option --predictions p_out to output the predictions to the file p_out. Then extract the labels from the training data using the following command-line:

for d in `seq 0 120`; do cat url_svmlight/Day$d.svm; done \
  |cut -d ' ' -f 1 |sed -e 's/^-1/0/' >labels

One can use Rich Caruana's perf software to compute the cumulative accuracy, but this requires a minor tweak in the code to allow more than 500000 predictions. Once that is dealt with, executing the command-line:

perf -ACC -files labels p_out

should give the result:

ACC    0.98364   pred_thresh  0.500000

98.36% accuracy by thresholding predictions at 0.5.