Gogrammer, a work-in-progress n-gram application written in Go.

This application creates a list of byte ngrams from a collection of files. There is an optional -hash flag which attempts to use a hashing algorithm, which should make larger values of N possible. It's inspired by this paper: Raff, E., & Nicholas, C. K. (2018). "Hash-Grams: Faster N-Gram Features for Classification and Malware Detection", available here.

The hash method seems to have about 60% of the resulting n-grams in common with normal ngramming, which could be attributed to hash collisions. However, the hash method seems to run in about a quarter of the time.

The ability to train based on a created dataset is new, but requires running with GODEBUG=cgocheck=0 to work, due to this bug in golearn.

Dependencies:

• go-rabin
• golearn

How to use it:

1. Find the n-grams in your dataset: ./gogrammer NGRAM /path/to/goodware /path/to/malware. Additional options are available, including changing the number of n-grams to keep, and the size of the n-grams.
2. Build a CSV or LibSVM dataset file based on the n-grams: ./gogrammer DATASET -goodware /path/to/goodware -malware /path/to/malware -kl output.grams.
3. Train the model: GODEBUG=cgocheck=0 ./gogrammer TRAIN -hasFlags -dataset dataset.csv -output my-model.model. Additional options are available. The model is saved using liblinear 's format.