MIDAS

Hi, I am one of the authors of the work. MStream detects anomalies, intrusions, DoS and DDoS attacks in real time and constant memory. It is built on top of MIDAS (https://github.com/Stream-AD/MIDAS/) and works in a multi-aspect data setting i.e., entries having multiple dimensions such as event-log data, multi-attributed graphs etc. MStream is two orders of magnitude faster while achieving higher accuracy on several publicly available datasets. Github Repository: https://github.com/Stream-AD/MStream

Thanks for your comments. I know the documentation and examples is still a bit raw, will be working on it. When doing the implementation, I was trying to mimic the original API(https://github.com/bhatiasiddharth/MIDAS) that was done in the C++ first. This was in case someone wants to use the exact same API(for familiarity) in Go, they could. Posted this here to show that there is now a Go implementation of it. :) After this, I'll be implementing a fit/predict api that mimics SKLearn's API and showing more examples on how to use it in a streaming fashion.

I adapted the code from the original paper from https://github.com/bhatiasiddharth/MIDAS/ . And from one of the issues(https://github.com/bhatiasiddharth/MIDAS/issues/7#issuecomme...), M can be any reasonable value because it's just used to compute the edge hash (https://github.com/steve0hh/midas/blob/master/edgehash.go#L4...). Hope it clarifies.

This is an R wrapper around the C++ implementation https://github.com/bhatiasiddharth/MIDAS MIDAS can help social networks like Twitter and Facebook detect fake profiles used for spam and phishing in real-time, at a speed many times greater than existing state-of-the-art models.

Code is available in C++, Python, Ruby, R, and Rust at https://github.com/bhatiasiddharth/MIDAS

Currently MIDAS is available in Rust, Python, Ruby and R at https://github.com/bhatiasiddharth/MIDAS. If someone is interested to convert MIDAS to other languages, please feel free to do so and let me know so that I can add a link in the repository.

We detect suddenly appearing bursts of activity which share many repeated nodes or edges, which we refer to as microclusters. E.g. denial of service (DoS) attacks in network traffic data and lockstep behavior. Also, we detect scenarios where an individual edge may not be anomalous but along with other edges it acts as an anomalous community. For example, in the animation at https://github.com/bhatiasiddharth/MIDAS/ it may be possible that an individual edge is not anomalous but together the three malicious entities do a coordinated DoS attack.

Git repo https://github.com/bhatiasiddharth/MIDAS > Up to 48% more accurate and 644 times faster than the state of the art approaches

Code and Datasets we used are available at https://github.com/bhatiasiddharth/MIDAS

Hi, here is the MIDAS github repository: https://github.com/bhatiasiddharth/MIDAS Few use cases include detecting intrusions, fake ratings and financial fraud.