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Generate fine-grained signatures in seconds,
even for zero-day attacks – without relying on out-of-date signature
databases.
Neural network technologies rapidly detect
the emergence of previously unseen and unknown threats.
Packet-based analysis eliminates reliance
on and weaknesses of flow-based systems.
Real-time analysis at gigabit speeds.
Designed to protect the world’s most demanding networks from disaster, Esphion enterprise (EX) and service provider (SP) appliances detect, in real-time, network anomalies and threats such as denial of service attacks (DDoS), worm outbreaks, and other threats, even if previously unseen and unknown. Esphion EX and SP appliances use neural network and behavior-based, real-time analysis provides a comprehensive view of network security through a user-friendly, web-based GUI. By continually profiling the behaviour of network traffic, normal behavior is learnt. Anomalies that have the ability to impact network availability or performance are instantly recognized. Without relying on signature databases or complex rule-sets, Esphion can detect even zero-hour events in just seconds, and through a patent-pending analysis process extract signatures for those events. These signatures can then be applied to already existing network infrastructure elements, such as routers, firewalls or intrusion prevention systems, in order to surgically remove the offending traffic, while allowing legitimate traffic to continue to flow. Esphion is used by leading enterprises and service providers to detect and eliminate: New, unseen and unknown threats such as worms
or DDoS attacks that routinely result in network disasters.
Inappropriate internal activity such as insider
attacks.
Violations of internal policies, such as excessive
peer-to-peer usage, that expose enterprises to legal risk and waste
internal resources.
Network mis-configurations, which result in performance
impacting network traffic anomalies.
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World-class Security Architecture Return on Investment Detect swarms.
More network defense.
Highly sensitive detection algorithms. More ›
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