07 Sep 2014

# Building an SMS spam filter

TL;DR

I am experimenting with a spam filter for SMS. You can demo an implementation here: kehers.github.io/experiments/snag. (Or download here: github.com/kehers/snag). If you are interested in the final app, get notified here: tinyletter.com/kehers

/TL;DR

Addressing SMS spam (there has been a lot of it lately) is a simple thing theoretically.

1. Listen to incoming messages
2. Detect if it is spam
3. Block it. Or save it elsewhere. Or delete it. Whatever.

1 and 3 are simple but 2 is a bit tricky. The easy way is to check if the sender id is in a list of senders that have been marked as spam. Or we can mark all SMS with a sender id that match a format, e.g numerical sender ids that is less than 8 characters, as spam.

This works. But one, you have to keep marking new sender ids as spam. And then, some senders use the same sender id to send spam and non-spam messages - the sender id MTN N for example. Blocking all sender ids less than 8 characters in length won’t work efficiently either. Many important messages come from sender ids less than 8. My Etisalat data messages for example.

What about we detect spam SMS a similar way to how emails do it? We can use a simple classification algorithm, Naive Bayes, that is also used by many email spam filters.

#### Naive Bayes

Naive Bayes uses conditional probabilities to detect the likelihood that a word (or group of) belong to a category. In simple form, let’s assume the word “now” appears 3 times out of 84 spam words and just once out of 250 non-spam words (let’s call this ham). What is the probability that a message with now is a spam or ham? Well we can say, the probabilty that it is spam is 3/84 = 0.0357 and the probability that it is ham (not spam) is 1/250 = 0.004. Obviously, 0.0357 is more than 0.004, so we can say it is spam.

But it is not that straightforward. Here we are assuming many things. The real formula is

``````P(spam/now) = P(now/spam) * P(spam) / (P(now/spam) * P(spam) + P(now/ham) * P(ham))
P(ham/now) = P(now/ham) * P(ham) / (P(now/ham) * P(ham) + P(now/spam) * P(spam))
``````

For our example, we are assuming equal probabilities that a message is spam or ham, i.e for every 2 messages, 1 is spam and the other is ham. This means P(spam) = P(ham) = 1/2. Then again, both denominators (called evidence) are the same. They are constants so we can ignore them. This therefore reduces the formula to

``````P(spam/now) = P(now/spam)
P(ham/now) = P(now/ham)
``````

Again, this is because we have just two categories - spam and ham and assuming equal probabilities of both.

#### Implementation

By using this algorithm, we can combine the probabilties of words in an SMS. This means we find the probability of each word in the text and combine (multiply) them. For this experiment, I am using a modified version called corrected probability to deal with [rare] words that may not exist in the training data.

There are other slight modifications here and there. One is ignoring words that are less than 3 characters in length. I still need to test if this is really necessary though. In SMS, every word seem to count - even stop words like a,at,be.

Another thing I am taking into consideration is case sensitivity of words. Therefore, the words free and FREE are considered different words and not the same. Spam messages use FREE (uppercase) a lot. A friend sending you a text with free will likely send it in lower case. This seem to improve the filter. (Still have to test extensively too).

#### Getting training data

We need an existing set of data to train (get words and occurrence counts for) our filter. I synced my device to my PC and copied some of the spam messages I have received. I also created a Google doc anyone can add to. I shared this on Twitter and the awesome people there have added to it.

#### What’s next?

I have done a simple implementation in Javascript here: kehers.github.io/experiments/snag. I used some of the spam and non-spam messages from my inbox as training data. You can check it out, and test with some SMS. It may not be 100% accurate yet as I used just a few training data. You can also download the source here: github.com/kehers/snag. It is just a few lines of Javascript. I am using this to test possible heuristics before implementing on mobile - android to start with. To be notified when it is completed, add your email here: tinyletter.com/kehers.

Do let me know what you think. You can buzz me on twitter via @kehers.

My name is Opeyemi Obembe. I build things for web and mobile and write about my experiments. Follow me on Twitter–@kehers.