Detecting 'unusual behavior' using machine learning with CouchDB and Python?

I also thought the approach using svm.OneClassSVM from sklearn was going to produce a good outlier detector. However, I put together some representative data based upon the example in the question and it simply could not detect an outlier. I swept the nu and gamma parameters from .01 to .99 and found no satisfactory SVM predictor.

My theory is that because the samples have categorical data (cities, states, countries, web browsers) the SVM algorithm is not the right approach. (I did, BTW, first convert the data into binary feature vectors with the DictVectorizer.fit_transform method).

I believe @sullivanmatt is on the right track when he suggests using a Bayesian classifier. Bayesian classifiers are used for supervised learning but, at least on the surface, this problem was cast as an unsupervised learning problem, ie we don't know a priori which observations are normal and which are outliers.

Because the outliers you want to detect are very rare in the stream of web site visits, I believe you could train the Bayesian classifier by labeling every observation in your training set as a positive/normal observation. The classifier should predict that true normal observations have higher probability simply because the majority of the observations really are normal. A true outlier should stand out as receiving a low predicted probability.

You're correct in assuming that this is a problem ideally suited to Machine Learning, and scikit-learn.org is my preferred library for these types of problems. Don't worry about specifics - (couchdb cloudant) for now, lets get your problem into a state where it can be solved.

If we can assume that variations in log-in details (time, location, user-agent etc.) for a given user are low, then any large variation from this would trigger your alert. This is where the 'outlier' detection that @Robert McGibbon suggested comes into play.

For example, squeeze each log-in detail into one dimension, and the create a log-in detail vector for each user (there is significant room for improving this digest of log-in information);

• log-in time (modulo 24 hrs)
• location (maybe an array of integer locations, each integer representing a different country)
• user-agent (a similar array of integer user-agents)

and so on. Every time a user logs in, create this detail array and store it. Once you have accumulated a large set of test data you can try running some ML routines.

So, we have a user and a set of log-in data corresponding to successful log-ins (a training set). We can now train a Support Vector Machine to recognise this users log-in pattern:

from sklearn import svm

# training data [[11.0, 2, 2], [11.3, 2, 2] ... etc]
train_data = my_training_data()

# create and fit the model
clf = svm.OneClassSVM()
clf.fit(train_data)

and then, every time a new log-in even occurs, create a single log-in detail array and pass that past the SVM

if clf.predict(log_in_data) < 0:
    fire_alert_event()
else:
    # log-in is not dissimilar to previous attempts
    print('log in ok')

if the SVM finds the new data point to be significantly different from it's training set then it will fire the alarm.

My Two Pence. Once you've got hold of a good training set, there are many more ML techniques that may be better suited to your task (they may be faster, more accurate etc) but creating your training sets and then training the routines would be the most significant challenge.

There are many exciting things to try! If you know you have bad log-in attempts, you can add these to the training sets by using a more complex SVM which you train with good and bad log-ins. Instead of using an array of disparate 'location' values, you could find the Euclidean different log-ins and use that! This sounds like great fun, good luck!