Automated Security Platform

STINGAR Overview

STINGAR (Shared Threat Intelligence for Network Gatekeeping with Automated Response) is a software platform comprised of a set of tools originally developed at Duke University as part of its overall IT security strategy. The STINGAR framework applies new, agile approaches to protect and defend enterprise networks, combining automated technology protections that deliver near-real-time active responses with crowdsourced data from many enterprise institutions in order to better secure those networks.

Enterprise Network diagram

Automated Security Platform

Deception Technology with automated response

STINGAR utilizes Deception Technologies to identify malicious actors in real time. Threat data is shared with partners to identify and block malicious actors automatically, even before they are seen on partner networks

Real time blocking of malicious attacks

STINGAR generates shared threat intelligence in real time, yielding actionable intel about attacks in progress, whether on your network or others, and enabling rapid protection against malicious IPs targeting multiple environments 

Measurable results in real world networks

STINGAR has improved blocking of attack attempts by two orders of magnitude, from an average of 10 million blocked network connections per day to an average of 280 million and at a height 1.7 billion blocked connections per day

Automation & integration with existing equipment

STINGAR utilizes automation integrated with existing network security tools such as Firewalls and IDS/IPS and Routers to block bad actors in real time and to report activity to industry standard SIEM log tools 

The Network effect: sharing Threat Intelligence benefits all

The STINGAR ecosystem comprises over 30 leading research institutions and the number continues to grow each month. STINGAR enables the federation of all shared threat data from all partners and employs network actuators to rapidly block threats in near-real-time. Data analysis shows that the majority (>50%) of threats are detected by other partners before threats arrive on individual partner networks 

Local detection provides more relevant results

STINGAR detection is more relevant and accurate than vendor-provided tools, with no reported false positives during several years of production operation on all partner networks

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