How Behavioral AI Detects Zero-Days Before CVEs Exist
Deep-dive into Ethereon's ensemble ML pipeline — Isolation Forest, LSTM, and graph-context models that catch novel exploits 48–72 hours before public disclosure.
Read more Blog
Research, threat intel & engineering deep-dives.
Primary research from the Ethereon team — zero-day analysis, behavioral AI, SOC playbooks, and the post-signature security era.
Federated Learning in Cybersecurity: Privacy by Design
Why Ethereon uses differential-privacy gradient sharing across the global fleet — and how customer data never leaves the tenant.
Read more80+ SOC Playbooks: From Detection to Containment in Under 3 Minutes
A practical walkthrough of Ethereon's autonomous response framework — from anomaly score to endpoint isolation.
Read moreRansomware Pre-Detonation: Catching Encryption in the First 200ms
File-system entropy analysis + process-tree behavioral profiling stops ransomware before the ransom note appears.
Read moreGraph-Aware Lateral Movement Detection
How Ethereon models east-west traffic as entity graphs to detect pivot attempts in real time.
Read moreMITRE ATT&CK Mapping in the Behavioral Detection Pipeline
Every Ethereon anomaly score maps to MITRE tactics and techniques — here's how we do it and why it matters for SOC teams.
Read moreIsolation Forest for Real-Time Anomaly Scoring at Scale
Technical deep-dive into how Isolation Forest provides fast, interpretable outlier detection across billion-event streams.
Read moreBeyond Rules: Behavioral Models for Credential Abuse Detection
Impossible-travel, MFA fatigue, token replay, and OAuth scope abuse — modeled as behavior, not static rule sets.
Read moreOne-Click Compliance: How the Audit Vault Works
Merkle-tree-backed tamper-evident logging for every detection, decision, and response action. ISO 27001, GDPR, HIPAA, SOC 2 ready.
Read moreSIEM Integration Deep-Dive: Splunk, Sentinel, Elastic, QRadar
Bidirectional connectors, HEC ingestion, and how Ethereon enriches your existing SIEM with behavioral anomaly context.
Read moreCase Study: How Ethereon Would Have Caught Log4Shell
A retrospective analysis applying Ethereon's behavioral pipeline to the Log4j vulnerability — detection 60 hours before the public PoC.
Read moreBuilding a Real-Time Threat Intelligence Feed with Behavioral AI
From raw telemetry to structured IOCs — the architecture behind Ethereon's premium threat-intel pipeline.
Read moreLSTM Sequence Models for Multi-Step Attack Chain Detection
How recurrent neural networks read time-ordered events to catch reconnaissance → escalation → exfiltration sequences.
Read moreAir-Gapped Deployment for Government and Defense Networks
Full on-prem mode with offline model deltas via signed bundles — Ethereon for classified environments.
Read moreUEBA for Insider Threat Detection: Beyond DLP
Per-entity behavioral baselines surface insider threats and compromised accounts without relying on data-loss-prevention rules.
Read moreTransaction-Graph Anomaly Detection for Banking
How Ethereon models transaction patterns as graphs to surface card fraud, ACH abuse, and wire fraud across accounts in milliseconds.
Read more5G Core Behavioral Profiling: AMF, SMF, UPF Monitoring
Telecom-specific behavioral models for 5G core network components — signaling-plane anomaly detection at scale.
Read moreAI-Phishing Defense: Deepfake-Aware Email and Voice Detection
Multi-modal models that detect AI-generated phishing attempts — text, voice, and video deepfakes.
Read moreContinuous Model Retraining with Analyst Feedback
How weekly model retraining incorporates SOC analyst feedback to drive false-positive rates down over time.
Read moreMedical Device Behavioral Fingerprinting for Healthcare
Behavioral profiling of CT scanners, MRI machines, and infusion pumps — catching compromised medical IoT devices.
Read more