In this blog, we walk users through identifying beaconing malware in their environment using our beaconing identification framework.

**Beaconing — A primer**

**Our approach**

**How do we do this using Elasticsearch?**

**Tuning parameters and filtering**

**Evaluation**

What's next

Identifying beaconing malware using Elastic

In this blog, we will briefly talk about how we fine-tuned a transformer model meant for a masked language modeling (MLM) task, to make it suitable for a classification task.

Preamble

NLP for command line classification

Fine-tuning a Hugging Face model

Importing custom models into Elastic

Using the Transformer model for inference — a series of experiments

Using multiple nodes and threads

Dynamic quantization

Knowledge Distillation

Dynamic quantization and knowledge distillation

Bringing it all together

Getting the Most Out of Transformers in Elastic

Author

Thomas Veasey

Articles

Identifying beaconing malware using Elastic

Getting the Most Out of Transformers in Elastic