Your firewall wasn't built
for malware that thinks.

Autonomous threats that rewrite themselves mid-attack are already here. Traditional defenses can't keep up.

{{ animatedThreats.toLocaleString() }}
Threats neutralized

So we built a defense that thinks different.

Your machine. Your model.

Every node builds its own personalized AI model — shaped by your traffic, your patterns, your threat surface. It learns what normal looks like for you, and acts autonomously when something breaks that pattern.

Every node warns every node.

When your Shield blocks a threat, that intelligence reaches every other Shield on the network. Thousands of nodes sharing real-time threat data. The more nodes join, the safer everyone gets.

5 minutes. Then forget about it.

One command. No tuning. Your Shield starts learning from the first packet, builds its model autonomously, and improves itself over time without manual intervention.

{{ stats.totalShields || '0' }} Shields protecting right now
99% Detection accuracy
99.9% Uptime — always on

See it in action

Two minutes. That's all it takes to understand why traditional defenses don't work anymore.

2:14

How Shield detects, learns, and shares — in real time

Protected in four steps

1

Deploy

One command on Linux or Windows. Under 5 minutes.

2

Learn

Shield maps your normal traffic. No config needed.

3

Protect

Anomalies detected and blocked before damage is done.

4

Multiply

Your Shield strengthens every other Shield. And theirs strengthens yours.

2,244 attacks per day hit the average unprotected server.

Most go unnoticed. Until the damage is done.

Attackers evolved. Agentic malware rewrites itself mid-attack, probes around the clock, and shares intelligence. Your static rules were written for last year's threats.
Defense should evolve too. Shield builds a personalized cognitive model for your machine. It detects and blocks autonomously. When it blocks, every Shield on the network blocks with it.
Isolation is the real risk. Every hour without Shield is an hour your server is alone — while every connected node gets smarter together.

You're not alone anymore

Every Shield that joins makes the network harder to attack. You benefit from every threat every other Shield has ever seen.

Your data never leaves

We share threat signatures, never your traffic. Built in the EU under GDPR. Your business data stays yours.

Minutes, not days

When one Shield detects a new threat, every Shield on the network knows within minutes. By the time a botnet scans its next target, it's already blocked everywhere.

What you're losing with a traditional WAF

Setup time
Hours to days Under 5 minutes
Reaction speed
100–500ms (damage already done) Instant — blocks at kernel level
Maintenance
Manual rules you'll forget to update Self-learning — adapts without manual rules
Intelligence
Isolated — your server is on its own Shared threat network — every node protects every node

Frequently asked questions

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Your legacy firewall was built for yesterday's threats.

Traditional protection systems can't adapt, can't learn, and can't share intelligence. The threats moved on. Your defense should too.

European-built · EU AI Act compliant · Free tier forever · Cancel anytime

My Shields

Manage your registered Shield instances.

Register New Shield

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New Shield Registered!

Important: Save these credentials now. The secret cannot be recovered!
{{ newShieldCredentials.shieldId }}
{{ newShieldCredentials.apiKey }}
{{ newShieldCredentials.secret }}

Configuration

Add this to your Shield's settings.json:

{
  "dasData": {
    "enabled": true,
    "serverUrl": "{{ origin }}",
    "shieldId": "{{ newShieldCredentials.shieldId }}",
    "apiKey": "{{ newShieldCredentials.apiKey }}",
    "secret": "{{ newShieldCredentials.secret }}",
    "region": "{{ newShieldForm.region }}"
  }
}

Your Shields

{{ shield.name }}
{{ shield.shieldId }} {{ shield.region }} {{ shield.status }}
{{ shield.reportCount || 0 }} Reports
{{ shield.lastSeen ? formatDate(shield.lastSeen) : 'Never' }} Last Seen

You haven't registered any shields yet.

Authentication Required

Please sign in to access AI Insights.

AI Insights

Neural network anomaly detection

No Shields Registered

Register a Shield to start monitoring with AI

{{ (hopfieldState.currentScore || 0).toFixed(0) }}
Threat Level {{ hopfieldState.currentTierName || 'Unknown' }}
0306080100
{{ ((hopfieldState.threshold || 0.3) * 100).toFixed(0) }}% Threshold
{{ hopfieldState.status || '-' }} Status
{{ (hopfieldState.dataPoints || 0).toLocaleString() }} Samples
{{ hopfieldState.storedPatterns || 0 }} Patterns

Feature Analysis

20 features
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{{ (hopfieldState.topContributors?.[name] || 0).toFixed(1) }}%
No data

Decision History

{{ hopfieldHistory.length }}
{{ decision.tierName }} {{ typeof decision.score === 'number' ? decision.score.toFixed(1) : decision.score }} {{ formatAITime(decision.timestamp) }}
Affected IPs (click to lookup):
{{ ip }}
No decisions yet

Affected IPs

{{ getAffectedIPs().length }} unique
{{ ip.address }} {{ ip.count }}x {{ getTierName(ip.maxTier) }}

No IPs flagged

IPs appear here when decisions are triggered
Hopfield Neural Network 20-dimensional analysis Gradient attribution {{ hopfieldState.rateLimitedCount }} rate limited

Loading AI data...

No AI Data Available

This Shield hasn't reported any Hopfield data yet. Make sure the Shield is running and connected to DasData.

How Shield AI Works

Understand the neural network behind autonomous threat detection

🧠

Neural Network Threat Detection

Qriton Shield uses a Modern Continuous Hopfield Network - the same architecture behind modern AI transformers - to detect threats in real-time without predefined rules.

📊

Pattern Learning

Shield learns what "normal" traffic looks like for your application, then detects anomalies that deviate from learned patterns.

🎯

4-Tier Response

Based on anomaly score, Shield takes graduated actions: Allow → Rate Limit → Challenge → Block. No false positive storms.

🔍

Explainable Decisions

Every decision shows WHY it was made. Gradient attribution reveals which features contributed most to the detection.

Autonomous Adaptation

Shield auto-escalates during attacks and de-escalates when threats subside. Night mode ensures protection during off-hours.

Complete AI Pipeline

📡 Traffic
📐 20 Features
🧠 Hopfield
📊 Score
🛡️ Action

What is a Hopfield Network?

A Hopfield Network is a form of recurrent neural network invented by physicist John Hopfield in 1982. It serves as content-addressable memory - it can store patterns and recall them even from partial or noisy input.

Classic Hopfield (1982)

  • Binary states (+1/-1)
  • Limited capacity (~0.14N patterns)
  • Hard categorization
  • Not differentiable

Modern Hopfield (Shield)

  • Continuous states [-1, 1]
  • Exponential capacity
  • Soft composition ("60% A, 40% B")
  • Fully differentiable

Energy Function (LogSumExp)

E(state) = -1/β × log(Σ exp(β × similarity(pattern_i, state))) + ½||state||²

Low energy = Traffic matches learned patterns (NORMAL)
High energy = Traffic doesn't match any pattern (ANOMALY)

💡 Fun Fact: The LogSumExp energy function is the same mechanism used in modern AI transformers (GPT, BERT). Hopfield Networks are considered the mathematical foundation of attention mechanisms.

The 20-Feature Vector

Shield extracts 20 carefully selected features from traffic across 3 layers, normalized to [-1, 1] using Z-score normalization.

Network Layer (L4)

FeatureDescriptionAnomaly Indicator
connections_totalTotal TCP connectionsSudden spike = flood
syn_countSYN packets sentHigh = SYN flood
established_countCompleted handshakesLow ratio = scanner
unique_ipsDistinct source IPsSudden spike = botnet
syn_ratioSYN / Total ratioHigh = incomplete connections
established_ratioEstablished / TotalLow = failed connections

Application Layer (L7)

FeatureDescriptionAnomaly Indicator
requests_per_minuteHTTP request rateHigh = HTTP flood
error_404_rate404 errors / TotalHigh = reconnaissance
post_ratePOST requests / TotalHigh = POST flood
endpoint_entropyRequest distributionLow = endpoint abuse
user_agent_varianceUA string diversityLow = bot fingerprint

Behavioral Features

FeatureDescriptionAnomaly Indicator
geo_entropyGeographic distributionLow = geo-targeted attack
interval_varianceTime between requestsLow = automated requests
payload_size_avgAverage data sizeExtreme = exfiltration

Explainable AI (XAI)

Shield doesn't just make decisions - it explains WHY using gradient-based attribution.

Example Decision

AI_STATUS Tier:3(BLOCK) Score:85.2 Threshold:0.32 | Features: SYN_Ratio=0.9(HIGH,25%) UniqueIPs=892(HIGH,20%) POSTs=450(HIGH,18%)

Interpretation:

  • SYN_Ratio contributed 25% to the anomaly detection
  • UniqueIPs contributed 20%
  • POST rate contributed 18%
  • Together these explain 63% of why this traffic was flagged

🔍 Gradient Attribution

Computes ∂E/∂feature to determine each feature's contribution to the anomaly score.

📜 Decision History

Full audit trail of every decision with timestamp, score, tier, and attribution.

🔗 IP Traceability

Track which IPs were affected by each decision and their full event timeline.

4-Tier Response System

0-30 ALLOW Normal traffic
30-60 RATE LIMIT Throttle 30/min
60-80 CHALLENGE Verify human
80-100 BLOCK Drop packets

Autonomous Adaptation

Shield operates in four adaptive modes, automatically switching based on attack intensity.

Mode SYN Timeout Anomaly Threshold HTTP/min Use Case
Relaxed 30s 0.85 1500 Low traffic periods
Balanced 20s 0.75 800 Normal operation
Aggressive 10s 0.60 400 Active attack
Lockdown 5s 0.45 200 Emergency

⬆️ Auto-Escalation

When blocks/minute > 50, Shield escalates to the next mode (relaxed → balanced → aggressive).

⬇️ Auto-De-escalation

When blocks/minute < 10 for 15 minutes, Shield de-escalates to a calmer mode.

🌙 Night Mode

Between 22:00-06:00, Shield enforces minimum "balanced" mode to protect during off-hours.

⚡ Velocity Detection

If 3+ IPs from the same /24 are blocked within 60 seconds, subnet threshold drops from 25 to 10.

Morris Counters: HLM-Inspired Infinite Scaling

Inspired by the Hopfield network's LogSumExp energy function, Shield uses Morris Counters for botnet detection that scales to infinity.

The Problem

During a massive botnet attack with 8,359+ IPs matching the same request pattern:

  • Linear counting: 33KB memory, 5.8 days to clean up
  • System would freeze or crash under load

The Solution: Logarithmic Counting

Instead of storing the actual count, store log₂(count):

Attack Size Linear Counter Morris Counter
8,359 IPs 33,436 bytes 13 bytes
1 Million IPs 4 MB 20 bytes
1 Billion IPs Would crash 30 bytes
2²⁵⁵ IPs Impossible 255 bytes

Morris Counting Algorithm

Counter value 'c' represents approximately 2^c actual events Increment rule: With probability 1/2^c, increment c by 1

c=0: Always increment (P=100%)
c=5: Increment with P=3.125%
c=13: Increment with P=0.01%

💡 Connection to Hopfield: Just as LogSumExp compresses exponential data to log scale in the energy function, Morris counters compress event counts to log scale - enabling infinite scaling with constant memory.

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OpenClaw Intel

v2 Threat Intelligence — Supply chain, semantic, exploit, behavioral, and credential threat feeds

{{ v2Stats.packageThreats || 0 }}
Package Threats
{{ v2Stats.semanticSignatures || 0 }}
Semantic Signatures
{{ v2Stats.exploitFingerprints || 0 }}
Exploit Fingerprints
{{ v2Stats.behavioralAnomalies || 0 }}
Behavioral Anomalies
{{ v2Stats.credentialIndicators || 0 }}
Credential Indicators

Tag Distribution

{{ tag }} {{ count }}

No v2 threat intelligence data yet. Shields with v2 enabled will start contributing data automatically.

Package Registry Malware Family Indicators Confidence Reports
{{ pkg.name }}{{ pkg.version ? '@' + pkg.version : '' }} {{ pkg.registry }} {{ pkg.malwareFamily }} {{ ind }} {{ (pkg.confidence * 100).toFixed(0) }}% {{ pkg.reportCount }} ({{ pkg.uniqueShields }} shields)

No package threats reported yet.

Threat Class Description Targeted Tool Confidence Reports
{{ sig.threatClass }} {{ sig.patternDescription || '—' }} {{ sig.targetedTool || '—' }} {{ (sig.confidence * 100).toFixed(0) }}% {{ sig.reportCount }} ({{ sig.uniqueShields }} shields)

No semantic signatures reported yet.

CVE Target Service Phase Description Confidence Reports
{{ exp.cve || '—' }} {{ exp.targetService || '—' }} {{ exp.exploitPhase || 'unknown' }} {{ exp.description || '—' }} {{ (exp.confidence * 100).toFixed(0) }}% {{ exp.reportCount }} ({{ exp.uniqueShields }} shields)

No exploit fingerprints reported yet.

Entity Type Deviation Type Severity Confidence Reports
{{ anom.entityType }} {{ anom.deviationType }} {{ anom.severity }} {{ (anom.confidence * 100).toFixed(0) }}% {{ anom.reportCount }} ({{ anom.uniqueShields }} shields)

No behavioral anomalies reported yet.

Account Settings

Manage your account information.

Danger Zone

Permanently delete your account and all associated data.

This action cannot be undone.

Your account will be scheduled for deletion. Per GDPR requirements, your data will be retained for 30 days for legal compliance, then permanently and irreversibly deleted.

All your Shields will be immediately revoked and will stop working.

{{ deleteError }}

Network Status

Live Threat Activity

{{ stats.totalThreats?.toLocaleString() || 0 }} Threat IPs
{{ stats.totalShields || 0 }} Active Shields
{{ stats.totalReports?.toLocaleString() || 0 }} Total Reports
{{ stats.avgThreatScore || 0 }} Avg Threat Score

Recent Threats

{{ threat.ip }} {{ threat.geo.country }}
{{ (threat.threatScore * 100).toFixed(0) }}% {{ cls }}
No threats reported yet

Top Threats

{{ threat.ip }} {{ threat.geo.country }}
{{ (threat.threatScore * 100).toFixed(0) }}% {{ threat.reportCount }} reports
No threats reported yet

Attack Classifications

{{ formatType(type) }} {{ count.toLocaleString() }}

IP Threat Lookup

{{ lookupResult.ip }} HIGH RISK MEDIUM RISK LOW RISK
{{ (lookupResult.threatScore * 100).toFixed(0) }}%
Threat Score

Classifications

{{ cls }}

Location

{{ lookupResult.geo.city || 'Unknown' }}, {{ lookupResult.geo.country }}

Network

{{ lookupResult.network.org }}

ASN: {{ lookupResult.network.asn }}

Activity

First seen: {{ formatDate(lookupResult.firstSeen) }}

Last seen: {{ formatDate(lookupResult.lastSeen) }}

Reports: {{ lookupResult.reportCount }} from {{ lookupResult.reporterCount }} shield(s)

Attack Patterns

{{ formatType(pattern) }} {{ data.count }} events

AI Detection

Hopfield Neural Network detected this IP in {{ lookupResult.hopfield.decisionCount }} decision(s). Latest action: {{ lookupResult.hopfield.latestTierName }}

{{ lookupResult.ip }} NOT FOUND AI DETECTED

This IP has no threat reports in our database.

This IP was detected by the Hopfield Neural Network in {{ lookupResult.hopfield.decisionCount }} decision(s). Latest action: {{ lookupResult.hopfield.latestTierName }}

Register Your Shield

Register your Qriton Shield instance to contribute threat intelligence to the global database.

How It Works

1

Sign In

Create an account with your email address to get started.

2

Register Shield

Register your Shield instance and receive API credentials.

3

Configure

Add the credentials to your Shield's settings.json file.

4

Protect

Your Shield will automatically report blocked threats to the global database.

Authentication Required

Please sign in to access Threat Feeds.

Threat Feeds

Download threat intelligence feeds to integrate with your security infrastructure.

IP Blocklist (JSON)

Complete threat IP list with scores and classifications

Download JSON

IP Blocklist (TXT)

Plain text list for firewall import

Download TXT

Subnet Blocklist

Aggregated /24 subnets with multiple threats

Download

Recent Threats

Latest reported threat IPs

Download

Authentication Required

Please sign in to access API Documentation.

API Documentation

Integrate Qriton Shield threat intelligence into your applications.

Public Endpoints

No authentication required. Rate limited to 60 requests/minute.

GET /api/lookup/{ip}

Look up threat data for a specific IP address.

curl {{ origin }}/api/lookup/1.2.3.4
POST /api/lookup/bulk

Look up multiple IPs at once (max 100).

curl -X POST {{ origin }}/api/lookup/bulk \
  -H "Content-Type: application/json" \
  -d '{"ips": ["1.2.3.4", "5.6.7.8"]}'
GET /api/feed/blocklist

Download the threat blocklist. Parameters: format (json/txt), minConfidence, minReports.

curl {{ origin }}/api/feed/blocklist?format=txt&minConfidence=0.8
GET /api/stats

Get global threat statistics.

curl {{ origin }}/api/stats

Shield Reporting

For registered Qriton Shield instances only. Requires HMAC authentication.

POST /api/report

Submit a threat report. Requires Shield authentication headers.

Headers:
  X-Shield-Id: shield_abc123
  X-Shield-Key: your-api-key
  X-Shield-Timestamp: 1705756800
  X-Shield-Signature: hmac-sha256-signature

Body:
{
  "ip": "1.2.3.4",
  "classifications": ["syn_flood", "scanner"],
  "attackPatterns": {
    "syn_flood": { "count": 150 }
  },
  "geo": { "country": "CN" }
}

Integration Examples

iptables (Linux)

#!/bin/bash
# Download and apply Qriton blocklist
curl -s {{ origin }}/api/feed/blocklist?format=txt | while read ip; do
  iptables -A INPUT -s $ip -j DROP
done

Windows Firewall (PowerShell)

$blocklist = (Invoke-RestMethod {{ origin }}/api/feed/blocklist).blocklist
foreach ($threat in $blocklist) {
  New-NetFirewallRule -DisplayName "Qriton Block $($threat.ip)" `
    -Direction Inbound -RemoteAddress $threat.ip -Action Block
}

Troll Mode Activity

Monitor attacker entertainment and deception activities across the Shield network.

{{ trollStats.total || 0 }} Total Troll Events
{{ trollStats.byType?.honeypot_capture || 0 }} Credentials Captured
{{ trollStats.byType?.tarpit || 0 }} Tarpit Traps
{{ trollStats.byType?.rickroll || 0 }} Rickrolls

Event Types

{{ getTrollIcon(type) }} {{ getTrollTypeName(type) }} {{ count.toLocaleString() }}
No troll events recorded yet

Recent Events

{{ getTrollIcon(event.eventType) }}
{{ event.ip }} {{ getTrollTypeName(event.eventType) }}
{{ formatDate(event.timestamp) }}
No recent troll events

Honeypot Captures

Credentials captured from attackers trying fake login pages

{{ capture.ip }} {{ capture.endpoint || '/login' }}
Username: {{ capture.credentials?.username || 'N/A' }} Password: {{ capture.credentials?.password || 'N/A' }}
{{ formatDate(capture.timestamp) }}
No honeypot captures yet

Shield Network

{{ shield.name || shield.shieldId.substring(0, 12) }} {{ shield.threatMode || 'balanced' }}
{{ shield.blocksToday || 0 }} Blocks Today
{{ shield.trollEventsToday || 0 }} Trolls Today
{{ shield.lastSeen ? formatDate(shield.lastSeen) : 'Never' }}
No active shields

What is Troll Mode?

Troll Mode is a collection of attacker entertainment and deception features that waste attackers' time and resources while gathering intelligence:

🎵
Rickroll

Redirect attackers to Rick Astley's famous video

🐢
Tarpit

Slow-drip responses that waste attacker connections

🍯
Honeypot

Fake login pages that capture attacker credentials

🔄
Redirect Loops

Infinite redirect chains to confuse scanners

🎭
Fake Vulnerabilities

Simulated exploits that lead attackers nowhere

🧩
Impossible CAPTCHA

CAPTCHAs that can never be solved

💣
Zip Bomb

Compression bombs for aggressive scanners

Request IP Whitelist Review

If you believe an IP address has been incorrectly flagged as a threat, you can request a review. We take false positives seriously and will investigate all legitimate requests.

Submit a Review Request

The IP address you believe was incorrectly flagged
We'll contact you about the review outcome
Optional - helps us verify legitimate services
The more detail you provide, the faster we can review your request
{{ whitelistError }}

Request Submitted

Thank you for your submission. We'll review your request and contact you at {{ whitelistForm.email }} within 5 business days.

Request ID: {{ whitelistRequestId }}

Review Process

1

Submit Request

Provide details about the IP and why it should be reviewed

2

Verification

We verify ownership and review the threat reports

3

Analysis

Our team analyzes the traffic patterns that triggered the flag

4

Decision

We'll notify you of the outcome and any actions taken

Common False Positive Causes

  • Security Scanners: Authorized vulnerability assessments can trigger detection
  • Monitoring Services: Health check probes from legitimate services
  • VPN Exit Nodes: Shared IPs where one user's behavior affects others
  • CDN/Proxy: Traffic aggregation can amplify patterns
  • Misconfigured Services: Unintentional high-frequency requests

Before Submitting

First, check the IP's current status using our IP Lookup tool to see what triggered the flag.

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