NETWORK SECURITY
Fast and accurate anomaly detection, with no expertise required
The ShieldIOT AI-based network anomaly detection solution analyzes device communications to identify the first signs of cyber-attacks and operational anomalies. Deployed at the IoT network operator / service provider cloud, the agent-less solution is highly scalable, protecting massive multi-application IoT networks against various threats including DDoS, unknown malware, ransomware, communication hijacking, node takeover and replication, replay attacks, data falsification and many more.
FEATURE HIGHLIGHTS
Unmatched Accuracy
ShieldIOT Corset-based AI anomaly detection is x5 to x20 more accurate compared to leading industry solutions
Real-Time Detection
From one week to one minute. No speed or accuracy trade-offs. Detect the first signs of attack before damage takes place.
Actionable Alerts
Attack type, duration, affected devices, score, severity, trigger features… Enabling automated or non-expert threat mitigation
Unlimited Scalabilty
Transforms huge volumes of big data into small data sets. Agent-less solution with no device or network changes
ACCURATE ANOMALY DETECTION
The ShieldIOT AI network security is based on coreset real time streaming anomaly detection. The solution analyzes network traffic and additional data sources, to detect the first signs of cyber-attacks, while providing unmatched out-of-the-box event forensic to facilitate threat mitigation actions.
ShieldIOT is 20x more accurate compared to other anomaly detection vendors
ShieldIOT reduces detection time from one week to only one minute
HOW IT WORKS !
The ShieldIOT AI network security is based on coreset real time streaming anomaly detection. The solution analyzes network traffic and additional data sources, to detect the first signs of cyber-attacks, while providing unmatched out-of-the-box event forensic to facilitate threat mitigation actions.
Data Processing
Data filtering, null completion, data sources joining, data enrichment, feature engineering
AI Engine
Coreset computing to reduce the data, followed by running a set of AI and machine learning detection models.
Anomaly Classification
Automatically generate event type, by combining patented labeling process with domain expertise
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