spam-prevention

How well does Django's anti-spam system in the comments framework work? Have you used it? What percentage of comment-spam does it prevent roughly? Is there anything else you do to help prevent comment spam on sites using the Django comments framework? By default Django only offers a honey pot field and a hash check for spam prevention. It stops a lot of spam but absolutely not all. I'd guess at maybe 50-70% or so. For a very small site I have with less than 50 unique visitors per day, that is all that is needed. For larger sites, you definitely need additional protection like Akismet. There's

Are there ways except CAPTCHAs for web apps like pastie.org or p.ramaze.net ? CAPTCHAs take too long for a small paste for my taste. You could try Honeypot CAPTCHAs . Essentially, you hide some form fields using CSS. Your users will never fill out the fields because they won't see them, but spam bots who don't support CSS will fill them out. On the server side you just ignore comments/pastes/etc. that have that field filled out. In addition to honeypots, you can use timestamps and one-time-tokens to stop replay attacks. Here's a discussion of combining the two techniques . Personally, on my

A year or so ago I decided to stop using the mailto tag because many times it is annoying to the user. It will bring up an email program that the user may not be using at the time, which may also take time to load and may not be what the user wanted to do when he/she clicked on what looked like a link. Instead I decided to just start displaying the email address without the mailto tag. I believe that both ways probably can produce spam. My questions are these, Is the mailto tag the accepted way to setup email links, or do good people/companies avoid them? Second, if they both create spam, is

What is the currently best method to detect spam ? especially on mobile text message. are there any resource or comparison analysis ? It's good to look into supervised learning techniques. There've been a number of studies where the Multinomial Naive Bayes Classifier has been used for spam email filtering with a lot of success. If it worked for spam email filtering, then it should work with SMS filtering. What you need is a huge dataset of example spam SMS texts and train the classifier with it. Also, it may be helpful to look into the Support Vector Machine, which; although less widely used

I'm trying to come up with a JavaScript email obfuscator to reduce the chance for spam in emails listed on a web site. Right now I've got a JavaScript based obfuscator that uses a combination of HTML encoding & JavaScript to convert an obfuscated email into a normal email transparently. What I do is this: Format the "mailto:" part of the href in links to be HTML encoded like: mailto: I also encode the email, replacing the @ sign with (a) , so that the email reads something like: stackoverflow(a)example.com I then use some JavaScript to decipher all mailto links which have this (a) sign in the

I have a forum on a website I master, which gets a daily dose of pron spam. Currently I delete the spam and block the IP. But this does not work very well. The list of blocked IP's is growing quickly, but so is the number of spam posts in the forum. The forum is entirely my own code. It is built in PHP and MySQL. What are some concrete ways of stopping the spam? Edit The thing I forgot to mention is that the forum needs to be open for unregistered users to post. Kinda like a blog comment. In a guestbook app I wrote, I implemented two features which prevent most of the spam: Don't allow POST as

My current honey pot on my registration form is an entry for phone number which is placed off screen using position: fixed; . It tricked a standard breed form filler, but doesn't work as well as I though. Also, the spambots immediately responded to changes in the form, meaning that this is automatic, what can I do. Note: I hate captchs since the make they users solve my problems which is never good. Build a really smart honeypot That may seem obvious, but here are a few tricks(Details later): Think Like a spam bot Assume that they are able to know what is on screen or behind other elements

Let's say you have access to an email account with the history of received emails from the last years (~10k emails) classified into 2 groups genuine email spam How would you approach the task of creating a neural network solution that could be used for spam detection - basically classifying any email either as spam or not spam? Let's assume that the email fetching is already in place and we need to focus on classification part only. The main points which I would hope to get answered would be: Which parameters to choose as the input for the NN, and why? What structure of the NN would most