Stop threats
before they spread
Network is healthy
Overview
auto-refresh 10s
Healthy Sequences
0
Malicious Sequences
0
Network Topology
Office Gateway
Riverside HQzarouter_DEMO01HQ
Connected Devices
workstation-09
192.168.1.109
612 ok
file-server-01
192.168.1.20
1840 ok
workstation-12
192.168.1.112
488 ok
reception-ipad
192.168.1.31
1204 ok
conf-room-tv
192.168.1.40
932 ok
hr-laptop-03
192.168.1.55
776 ok
Live product preview · anonymized sample network
Technology
Built for the edge
Network Traffic Analysis
Real-time behavioral analysis of network traffic to detect malicious activity and anomalous communications before damage occurs.
Process Behavior Analysis
Continuous monitoring of process activity to identify malicious behavior and exploit patterns before they cause damage.
Zero-Day Protection
Machine learning models trained to recognize novel malware variants through behavioral fingerprinting, not signatures.
How it works
Detect. Decide. Respond.
Deploy
A lightweight agent runs on any Linux device at the edge. Minimal footprint, zero configuration.
Observe
Continuous behavioral monitoring builds an environment-specific baseline — no rules to write, no signatures to update.
Detect
AI models score activity in real time, identifying anomalies that signature-based tools never see.
Respond
Threats are contained automatically. Your team is alerted with full context — not a flood of raw alerts.
Use cases
Built for every edge
Network-wide threat visibility
Deploy across subscriber CPE to detect botnets, DDoS staging, and compromised devices before they affect your infrastructure. Aggregate behavioral telemetry across millions of endpoints.
- CPE anomaly detection
- Botnet identification
- Subscriber protection SLA
Research
Backed by peer-reviewed research
calBERT: Securing Routers Serving IoT Networks using Contrastive Augmented Learning
J. Carter, S. Mancoridis, P. Protopapas, B. Mitchell, B. Lilley
contrastBERT: Behavioral Anomaly Detection for Malware using Contrastive Learning
J. Carter, S. Mancoridis, P. Protopapas, B. Mitchell
sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings
J. Carter, S. Mancoridis, P. Protopapas
Malware Detection in Cloud Native Environments
B. Mitchell, A. Chandnani, J. Carter, D. Roumelioti, S. Mancoridis
Behavioral Malware Detection using Language Model Classifier Trained on sys2vec Embeddings
J. Carter, S. Mancoridis, P. Protopapas, E. Galinkin
Team
Who we are
John Carter, PhD
Co-Founder
Research in behavioral malware detection and contrastive learning at Drexel University.
john@zeroanomaly.com
Brian Mitchell, PhD
Co-Founder
Technology leader and cybersecurity researcher with over 30 years of industry and academic experience.
brian@zeroanomaly.com
Spiros Mancoridis, PhD
Co-Founder
Auerbach Berger Endowed Chair in Cybersecurity and Distinguished Professor of CS at Drexel University.
spiros@zeroanomaly.com
Pavlos Protopapas, PhD
Co-Founder
Scientific Program Director at the Institute for Applied Computational Science, Harvard University.
pavlos@zeroanomaly.comContact
Get in touch
Tell us about your environment — we'll show you what behavioral detection finds.