SAN DIEGO, May 18, 2020 /PRNewswire-PRWeb/ -- PolySwarm announced today the release of PolyScore, a threat scoring algorithm that provides the probability a given file contains malware in a single, authoritative number.
PolyScore has been designed to address some of the main shortcomings associated with crowdsourced models and existing multiscanners:
- Multiple and often conflicting binary opinions require additional, intuition-based work from analysts; which is time intensive, produces inconsistent results and can not be automated.
- Scores found in solutions like VirusTotal use basic models that simply summarize results by aggregating opinions; a suboptimal approach for identifying new and emergent threats.
PolyScore's algorithm filters the noise and amplifies the signal by weighting engine's opinions based on recent past performance, strengths, confidence levels, and other rich contextual threat indicators built from millions of daily assertions generated inside PolySwarm.
"As the volume and complexity of cyber threats increase, contextualizing and prioritizing incidents becomes more critical. We developed PolyScore to enable SOC and CTI teams to make quick defensive decisions at scale, with unprecedented accuracy," stated Paul Makowski, CTO of PolySwarm.
PolyScore uses a semi-supervised machine learning model to continuously improve over time, and already outperforms any other methods by a significant margin, currently yielding a 97% accuracy rate. Scan a file at https://polyswarm.network/ to see PolyScore in action.
About PolySwarm:
PolySwarm is a more effective way to detect, analyze and respond to new and emerging malware, the type of threats more likely to go undetected by existing solutions. PolySwarm is a launchpad for new technologies and novel threat detection methods, where commercial solutions and independent researchers compete to detect threats, and get compensated based on performance.
SOURCE PolySwarm
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