Email Spam Filter Performance: 95.20% F1 Score, 94.23% Precision

Executive Summary

OpenEFA is an AI-powered email security platform that uses multi-layered analysis to detect spam, phishing, and malicious emails. Our advanced scoring system combines traditional authentication (SPF, DKIM, DMARC) with AI-powered behavioral analysis, DNS validation, and machine learning to provide industry-leading protection.

Over the past 30 days, OpenEFA has analyzed 32,947 emails with a 95.20% F1 Score and 94.23% precision. The system safely delivered 57.1% to inboxes, quarantined 4.0% for review, and auto-deleted 35.9% as high-confidence spam—all with <2 second processing time. Deployed across 28 protected domains serving 381 recipients, OpenEFA proves that AI-powered email security can deliver enterprise-grade protection at a fraction of the cost.

Key Metrics at a Glance

Metric	OpenEFA Value	Industry Standard	Status
F1 Score	95.20%	85-92%	Above Average
Spam Detection Rate	96.43%	90-95%	Above Average
False Positive Rate	3.77%	15-25%	85% Better
Precision	94.23%	88-93%	Above Average
Emails Processed (30 days)	32,947	N/A	Production Scale
Daily Volume	~1,088 emails/day	N/A	Peak: 1,542 emails/day

Understanding F1 Score: 95.20%

The F1 Score is the single best measure of email security effectiveness, combining both precision and recall into one metric.

What This Means In Practice:

Out of 100 spam emails: OpenEFA catches 96
Out of 100 emails flagged: 94 are actually spam
Balance: Strong precision with high detection rate

Industry Comparison

Most commercial solutions: 85-92% F1 Score
Barracuda: ~90%
Mimecast: ~92%
Proofpoint: ~93%
OpenEFA (March 2026): 95.20% ✅ Above average performance

F1 Score Breakdown

95.20%

Overall F1 Score

Precision: 94.23%

Recall: 96.43%

Email Processing Breakdown (30 Days)

Disposition	Count	Percentage	Description
Delivered (Safe)	18,827	57.1%	Clean emails delivered safely to recipient inboxes
Quarantined (Review)	1,313	4.0%	Suspicious emails held for user review and release
Auto-Deleted (Spam)	11,817	35.9%	High-confidence spam automatically removed
Released	955	2.9%	User-released from quarantine
Total Analyzed	32,947	100%	All emails processed by OpenEFA

Protected Infrastructure

Protected Email Domains	28
Protected Recipients	381
Active Users	100+
Blocking Rules	3,096
Unique Sender Domains Analyzed	5,065

Average Spam Scores by Disposition

Delivered Emails	1.10	Low risk
Quarantined Emails	44.34	High-risk spam
Auto-Deleted	54.47	Very high-risk spam
Released	-9.12	False positives (trusted)
Overall Average	21.74	System baseline

Key Insight: The 43.24-point difference between delivered and quarantined emails demonstrates excellent separation between legitimate and malicious content.

Confusion Matrix (30-Day Period)

		Predicted
		Spam	Clean
Actual	Spam	11,576 True Positive	413 False Negative
Actual	Clean	814 False Positive	18,091 True Negative

What These Numbers Mean:

True Positives (11,576): Spam correctly identified and blocked
True Negatives (18,091): Clean emails correctly delivered
False Positives (814): Clean emails quarantined (recoverable)
False Negatives (413): Spam that slipped through

Derived Metrics:

Accuracy: (11,576 + 18,091) / 32,947 = 95.62%
Precision: 11,576 / (11,576 + 814) = 94.23%
Recall: 11,576 / (11,576 + 413) = 96.43%
Specificity: 18,091 / (18,091 + 814) = 96.23%

Spam Score Distribution (30 Days)

OpenEFA uses a graduated spam scoring system where each email receives a cumulative score based on multiple risk factors. Understanding score distribution helps evaluate system effectiveness and threshold tuning.

Score Range	Risk Level	Count	Percentage	Typical Action
0 - 5.9	Safe	17,335	52.6%	✅ Delivered
6.0 - 9.9	Suspicious	1,118	3.4%	⚠️ Quarantined
10.0 - 14.9	High Risk	1,220	3.7%	🛑 Quarantined
15.0+	Very High Risk	13,273	40.3%	❌ Auto-Deleted

Intelligent Thresholds

OpenEFA uses adaptive, multi-factor thresholds to determine email disposition. Emails are classified as delivered, quarantined, or auto-deleted based on cumulative scoring across all analysis modules.

52.6%

Clean Email (Safe)

7.1%

Suspicious (Quarantine)

40.3%

High-Risk Spam (Deleted)

Top Blocked Threat Types

Threat Type	Count	Description
DNS/Authentication Failures	13,037	SPF/DKIM/DMARC failures
Phishing Attempts	13,037	Credential harvesting, fake login pages
RBL Blocklist Matches	13,014	Known spam sources
BEC (Business Email Compromise)	12,927	Payment requests, wire fraud, executive impersonation
Backscatter/Auto-Reply Spam	1,646	Bounce spam, auto-reply abuse

Machine Learning Performance

OpenEFA's ML ensemble model uses multiple classifiers trained on production email data to provide adaptive spam detection.

Ensemble Model Metrics

Training Samples	8,750
Training Balance	4,375 spam / 4,375 ham
ML Accuracy	81.9%
ML F1 Score	82.7%
ML ROC AUC	91.2%
Features	130

Base Model Performance (ROC AUC)

XGBoost	91.0%
Random Forest	90.0%
Logistic Regression	85.8%

Ensemble Strategy: Multiple models are combined using stacking to achieve higher accuracy than any individual model.

System Performance

<2s

Avg Processing Time

99.9%

System Uptime

~2.5GB

Memory Footprint

5,000+

Daily Capacity

Volume Statistics (30 Days)

Daily Average: 1,088 emails/day
Peak Day: 1,542 emails

Minimum Day: 516 emails
Total Processed: 32,947 emails

How OpenEFA Spam Scoring Works

OpenEFA uses a multi-module scoring system where each analysis component contributes to the final spam score. This layered approach provides comprehensive threat detection while minimizing false positives.

1. Email Authentication Module

Validates sender authenticity using industry-standard protocols:

SPF: Verifies sending server is authorized
DKIM: Cryptographic signature validation
DMARC: Policy enforcement

Scoring:

✅ All pass: Score reduced (trusted)
⚠️ Partial: Neutral
❌ Failed: Score increased (high risk)

2. DNS Analysis Module

Advanced DNS validation and domain reputation:

RBL Checks: Multiple blocklist sources
Domain Spoofing: Multi-domain validation
PTR Records: Reverse DNS verification
Domain Age: New domain flagging

Scoring:

✅ Clean reputation: No impact
⚠️ Minor issues: Low increase
🛑 RBL listed: Moderate increase
❌ Spoofing detected: Significant increase

3. Phishing Detection Module

AI-powered analysis of phishing indicators:

Suspicious URL patterns (shortened, obfuscated)
Brand impersonation detection
Urgency language analysis
Credential harvesting indicators
Look-alike domain detection

Scoring:

✅ No indicators: No impact
⚠️ Low confidence: Low increase
🛑 Medium confidence: Moderate increase
❌ High confidence: Significant increase

4. Business Email Compromise (BEC)

Detects executive impersonation and wire fraud:

Display name spoofing detection
Payment request indicators
Urgency/secrecy language analysis
Executive title spoofing

Scoring:

✅ No BEC indicators: No impact
⚠️ Low confidence: Low increase
🛑 Medium confidence: Moderate increase
❌ High confidence: Significant increase

5. Behavioral Analysis Module

Analyzes sender behavior patterns and anomalies:

First contact detection
Sender reputation analysis
Graph-based relationship analysis

Scoring:

✅ Normal behavior: No impact
⚠️ Minor anomalies: Low increase
🛑 Significant anomalies: Moderate increase
❌ Severe anomalies: High increase

6. ML Ensemble Module

Adaptive learning from user feedback:

Multi-model ensemble voting
Confidence-weighted adjustments
Learns from released emails (false positives)
Learns from deleted spam (true positives)

Scoring:

✅ Ham prediction: Score reduced
⚠️ Uncertain: No impact
❌ Spam prediction: Score increased

How OpenEFA Compares

Metric	OpenEFA	Barracuda	Mimecast	Proofpoint
F1 Score	95.20%	~90%	~92%	~93%
Spam Detection	96.43%	~95%	~96%	~97%
Precision	94.23%	~89%	~91%	~94%
False Positive Rate	3.77%	~12%	~10%	~8%
Cost (50 users/year)	$199-799	~$3,000	~$4,800	~$7,200
Privacy-First AI	✅ Yes	❌ No	❌ No	❌ No

Key Advantages

✅ Above-average accuracy (95.20% F1 Score)
✅ Strong precision (94.23%)
✅ Low false positive rate (3.77%)
✅ 60-80% cost savings vs. commercial

✅ Full transparency (detailed scoring)
✅ Data sovereignty (self-hosted)
✅ No vendor lock-in
✅ Continuous learning system

Data Quality & Methodology

Measurement Period

Start Date: January 30, 2026
End Date: March 1, 2026
Duration: 30 days
Total Emails: 32,947
Environment: Production deployment (28 domains, 381 recipients)

Classification Methodology

Spam Threshold: Score ≥ 18.0
Clean Threshold: Score < 6.0
Validation: User quarantine actions (releases)
Source: Production MySQL database

Why These Numbers Matter

This 30-day period represents OpenEFA's production performance with fully operational detection modules including multi-module spam scoring with 20+ detection components, AI-powered NLP analysis using spaCy en_core_web_lg, machine learning ensemble with adaptive learning, and real-time DNS and authentication validation.

Note: These statistics represent real production data from OpenEFA deployments across multiple client domains. All metrics are verifiable and reproducible from the source database.

Real-World Email Security Performance: 95.20% F1 Score Accuracy

Executive Summary

Key Metrics at a Glance

Understanding F1 Score: 95.20%

What This Means In Practice:

Industry Comparison

F1 Score Breakdown

95.20%

Email Processing Breakdown (30 Days)

Protected Infrastructure

Average Spam Scores by Disposition

Confusion Matrix (30-Day Period)

What These Numbers Mean:

Derived Metrics:

Spam Score Distribution (30 Days)

Intelligent Thresholds

52.6%

7.1%

40.3%

Top Blocked Threat Types

Machine Learning Performance

Ensemble Model Metrics

Base Model Performance (ROC AUC)

System Performance

<2s

99.9%

~2.5GB

5,000+

Volume Statistics (30 Days)

How OpenEFA Spam Scoring Works

How OpenEFA Compares

Key Advantages

Data Quality & Methodology

Measurement Period

Classification Methodology

Why These Numbers Matter

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