4
$\begingroup$

My app receives messages with a random number of bits at a random time. But two weeks ago I started to notice some almost regular patterns on the metrics of my app. I suspect they are some bots sending artificially generated data to my app. Specifically, I'm looking for sequential subsets of messages in a time series where messages has almost the same number of bits.

I read about some methods but they use data where time is not a random variable. I appreciate any help you can provide, including books, web pages, tutorials (in Python if possible), etc.

$\endgroup$
  • $\begingroup$ I was looking for a solution and I found in the book Bayesian Methods for Hackers an example "Inferring Behavior from Text-Message Data". Maybe what I need to find the switchpoint in the time series. Like in this question in stackoverflow. What do you people think? Is there another method? $\endgroup$ – jocerfranquiz May 24 '16 at 21:42
  • $\begingroup$ Welcome to Datascience.SE! It's not so much a change detection problem as an anomaly detection problem. Here is a presentation. $\endgroup$ – Emre May 26 '16 at 7:05
0
$\begingroup$

As a first step, to segregate the messages that appear to be a bot, you could first try binning by message size. For example, if messages sent by bots are likely to be around 128 bytes to 140 bytes, assign these to a unique bin.

Next, create a time series based on this bin. Try to decompose the time series using an additive or multiplicative method such as Holt Winters. A strong seasonal component would help you identify regular and repetitive messages which are being generated automatically.

|improve this answer|||||
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.