CounterFlow AI Enters OEM Deal with Napatech for Deep Packet Capture to AI-driven Threat Detection

COPENHAGEN, Denmark, August 13, 2018 - Napatech(TM) (OSLO: NAPA.OL), the leading provider of reconfigurable computing platforms, today announced that cybersecurity innovator CounterFlow AI, which uses streaming machine learning technology at the network edge to help threat hunters quickly detect and respond to network threats, has announced that it has entered into an OEM agreement with Napatech, the leading provider of reconfigurable computing platforms. Counterflow will license Napatech's Pandion network traffic recording solution.

Counterflow AI Lead Sponsor Announced for SuriCon 2018

Counterflow AI Lead Sponsor Announced for SuriCon 2018

Cybersecurity startup CounterFlow AI, which uses streaming machine learning technology at the network edge to help cyber defenders quickly detect and respond to network threats, has been announced as the lead sponsor for the fourth annual SuriCon 2018, a conference dedicated to Suricata and open source security technologies, projects, and initiatives.

Dr. Andrew Fast to speak on the topic of Real-time, Streaming Anomaly Detection for Cybersecurity with Redis-ML, at the Applied Machine Learning Conference on April 12th in Charlottesville, VA.

Network-based threats to personal information, company secrets and critical infrastructure are increasing in frequency and sophistication. A rapid response is critical for stopping these threats, but, with network speeds reaching 100Gb/s and beyond, network defenders are swamped by overwhelming amounts of traffic, hindering both detection and response. Data scientists maintain that machine learning has the potential to help fight these threats, but the current dominant “Big Data” strategy of backhauling data to a central repository, using tools such as Hadoop and Spark, adds significant latency while incurring additional costs for bandwidth, storage, and computational resources. Furthermore, “Big Data” platforms such as Spark are optimized primarily for data management and model training, not serving trained models in deployment. We will walk through the process of translating a multi-dimensional anomaly detection algorithm that operates in batch into a streaming algorithm suitable for deployment in a network sensor. Then we demonstrate how deploying these models with Redis and the Redis ML module leads to dramatic reduction in processing time, leading to the faster threat detection and response.