Abnormal Blog

Dan is the Head of Machine Learning at Abnormal Security, where he builds systems to catch cyberattacks. Before Abnormal, Dan worked at Twitter, first as a staff machine learning engineer in Cortex, and later as the manager of the web ads machine learning team. Before Twitter, Dan worked as a senior data scientist at Truemotion, where he developed smartphone sensor algorithms to price car insurance.

An Abnormal Approach to Machine Learning: Feature Systems and Language Models

Dan Shiebler

Discover how the Abnormal attack detection team utilizes feature systems, advanced language models, and per-customer understanding in our approach to machine learning in cybersecurity.

How Abnormal Enhanced Its Detection Platform with BERT Large Language Models (LLMs)

Dallas Young

We are excited to share that Abnormal has recently deployed a BERT Large Language Model (LLM), pretrained from Google on a large corpus of data, and applied it to stop advanced attacks.

Abnormal Engineering Stories, Episode #9: Productionizing Machine Learning

Dan Shiebler

In episode 9 of Abnormal Engineering Stories, Dan sits down with Mukund Narasimhan to discuss his perspective on productionizing machine learning.

8 Key Differences Between Ineffective and Effective Machine Learning Engineers

Dan Shiebler

At Abnormal, we pride ourselves on our excellent machine learning engineering team. Here are some patterns we use to distinguish between effective and ineffective ML engineers.

Model Understanding with Feature Importance

Dan Shiebler

Here at Abnormal, our machine learning models help us spot trends and abnormalities in customer data in order to catch and prevent cyberattacks.