Author: @hejhdis
Abstract
Modern network defense relies heavily on rule-based scanners and predefined signatures to identify exposure and risk. Tools such as Nmap, OpenVAS, Nessus, and similar platforms are extremely powerful, yet they fundamentally operate on static knowledge: known ports, known services, known vulnerabilities.
This article is about speeding up defensive security analysis by applying machine learning to pre-collected, authorized Nmap scan results.
The approach recognizes a fundamental security principle: offense is the best defense. To defend effectively, we must understand how attackers think and what they look for. Therefore, this system incorporates offensive reconnaissance patterns into its defensive architecture.
The ML model trains on a pre-aligned list of Nmap scan commands and their outputs—commands that represent both defensive assessment and offensive reconnaissance techniques. By learning from these patterns, the system can:
- Identify what an attacker would see when scanning your network
- Detect exposures that offensive tools would exploit
- Prioritize remediation based on offensive value to attackers
- Accelerate the time from scan to actionable defense
Key principle: We use offensive scanning techniques (authorized, on our own networks) to build better defenses. The goal is defensive readiness through offensive awareness.
This article is intended as a technical exploration and research proposal. Community feedback is explicitly requested.
1. Background: How Network Defense Works Today
Most defensive network assessment tools follow a similar workflow:
- Scan hosts and networks
- Identify open ports and services
- Match service versions against known vulnerability databases
- Generate a risk or severity report
This approach is effective, but it has limitations:
- It treats all environments similarly
- It does not adapt to local network context
- It produces large volumes of alerts
- It struggles with configuration drift and subtle exposure changes
These tools answer: "Is this service known to be vulnerable?"
But they often fail to answer: "Is this exposure normal or abnormal for this network?"
2. The Core Idea: ML on Top of Scan Results (Not Instead of Them)
The idea explored here is not an AI-based scanner, but a post-scan analysis layer.
Key principle:
- Scanning remains deterministic
- Machine learning is applied only to interpretation
In this model:
- Nmap (or similar tools) performs scanning
- Scan outputs are parsed into structured features
- An ML model evaluates patterns, not exploits
- The system outputs risk context, not attack instructions
This keeps the system firmly in the defensive domain.
3. Why Rules Alone Are Not Enough
Rule-based systems are excellent for known bad conditions, such as:
- Deprecated protocols
- Exposed administrative services
- Services with confirmed CVEs
However, many real-world incidents occur due to:
- Misconfiguration
- Unexpected service exposure
- Gradual security drift
- Human error
- Environment-specific assumptions
Examples:
- A development service accidentally exposed in production
- A port that is "safe" individually but dangerous in combination
- A host behaving differently than its historical baseline
These are areas where pattern recognition may help.
4. What the ML Model Actually Learns
A common misconception is that the ML model "knows security".
It does not.
Instead, it learns statistical patterns from scan-derived features, such as:
- Number of open ports
- Categories of exposed services
- Presence of administrative interfaces
- Service diversity
- OS fingerprint distribution
- Changes over time
The model does not decide what is vulnerable.
It learns what is normal vs unusual within a given environment.
5. Hybrid Design: Rules + ML (Not ML Alone)
A critical design choice is hybrid architecture.
Rule Engine handles:
- Known risky ports
- Policy violations
- Compliance checks
ML Engine handles:
- Severity estimation
- Exposure patterns
- Anomaly detection
- Environment-specific baselines
This mirrors how experienced security teams operate: rules for certainty, analytics for context.
6. Anomaly Detection as a Defensive Strength
One promising application is anomaly detection.
Instead of asking: "Is this port dangerous?"
The system asks: "Is this exposure unexpected compared to past behavior?"
This allows detection of:
- Sudden port exposure
- Unusual service combinations
- Drift from hardened baselines
- Shadow services
Such signals are often early indicators of misconfiguration or compromise.
7. Intended Output (What This System Should and Should Not Do)
It SHOULD:
- Assign risk scores
- Explain contributing factors
- Highlight deviations from baseline
- Suggest defensive review actions
It SHOULD NOT:
- Recommend exploits
- Automate attacks
- Bypass security controls
- Replace vulnerability scanners
This distinction is essential to keep the system ethical and defensible.
8. How This Differs from Existing Tools
| Aspect | Traditional Scanners | Proposed Concept |
|---|---|---|
| Knowledge base | Static | Adaptive + learned |
| Context awareness | Low | Environment-specific |
| Anomaly detection | Limited | Core feature |
| Learning over time | No | Yes |
| Purpose | Detection | Interpretation |
This concept is complementary, not competitive.
9. Why This Is Not Widely Deployed (Yet)
There are valid reasons this approach is not common:
- Labeling data is expensive
- False positives can be costly
- Security teams prefer deterministic results
- ML adds operational complexity
This article does not claim these challenges are solved — only that they are worth discussing.
10. Ethical and Legal Considerations
This concept:
- Uses authorized scans only
- Operates on owned or permitted networks
- Focuses on risk awareness
- Keeps humans in the decision loop
It is explicitly not designed for offensive use.
11. Request for Community Feedback
This article is intentionally exploratory.
I would welcome feedback on:
- Whether ML adds real value here
- Better feature engineering ideas
- Ways to reduce false positives
- Similar research or tools I may have missed
- Practical pitfalls from real SOC environments
Criticism is encouraged.
12. Conclusion
Traditional network scanners are powerful, but they answer only part of the defensive question. As networks grow more complex and dynamic, defenders may benefit from systems that learn their environment, not just compare it to global rules.
Whether ML-assisted risk assessment becomes practical remains an open question — but it is one worth exploring collaboratively.
13. Multi-Phase Scanning: Using Multiple Defensive Scan Profiles per Target
Rather than relying on a single scan, this concept proposes multi-phase scanning, where a target is analyzed using multiple predefined, defensive scan profiles. Each profile is designed to observe the network from a different angle, without performing exploitation or intrusive actions.
This approach reflects how real security teams operate: by collecting diverse signals and correlating them before making conclusions.
Why Multiple Scans Matter
A single scan provides limited context.
Multiple scans allow the system to:
- Cross-validate exposure
- Reduce false positives
- Identify inconsistencies
- Detect configuration drift
- Build a richer feature set for analysis
The goal is observation, not attack.
14. Predefined Defensive Scan Profiles (Conceptual)
Below is an example of already-defined, safe scan categories that could be applied to a given target. These scans are read-only assessments.
⚠️ These are conceptual examples for defensive analysis and assume proper authorization.
1. Basic Exposure Scan
Purpose: Identify commonly exposed services.
- Open TCP ports
- Service names
- Basic service fingerprints
Used to establish a baseline exposure map.
2. Service Enumeration Scan
Purpose: Understand what is running, not exploit it.
- Service versions
- Application types
- Protocol distribution
Used to detect:
- Unexpected services
- Version inconsistency
- Shadow services
3. OS & Platform Fingerprinting
Purpose: Identify operating system patterns.
- OS family estimation
- Device type hints
- Platform consistency
Used to detect:
- Misidentified assets
- Rogue devices
- Inventory mismatch
4. Policy-Oriented Scan
Purpose: Compare exposure against internal policy.
- Administrative ports
- Legacy protocols
- Remote management interfaces
Used to flag policy deviations, not vulnerabilities.
5. Temporal / Repeated Scan
Purpose: Observe change over time.
- Port appearance/disappearance
- Service changes
- Exposure drift
Used to detect:
- Configuration drift
- Accidental exposure
- Unauthorized changes
15. Feature Correlation Across Scans
Each scan produces partial observations.
The ML system does not treat scans independently.
Instead, it:
- Aggregates results
- Correlates patterns
- Builds a unified feature representation per host
Example correlated features:
- Stability of exposed ports
- Frequency of service changes
- Risk concentration across scans
- Deviation from historical norms
This correlation step is critical — this is where ML adds value.
16. ML as a Defensive Analyzer, Not a Scanner
In this design, the ML model is explicitly not a scanner.
Its role is to act as a defensive analyzer that:
- Interprets scan results
- Assigns contextual risk
- Highlights unusual patterns
- Filters noise
The ML model does not:
- Suggest exploits
- Trigger attacks
- Automate blocking actions
Instead, it produces decision support signals for humans.
17. Output: Developer- and Defender-Friendly Summaries
One of the core goals of this concept is useful output, not raw scan data.
Rather than overwhelming users with port lists, the system extracts actionable summaries.
Example Summary Output (Conceptual)
Overall Risk: Medium
Confidence: 0.78
Key Observations:
• Administrative service exposed beyond baseline
• Unusual combination of services detected
• Port exposure changed since last scan
Potential Impact:
• Increased attack surface
• Policy deviation
• Possible misconfiguration
Recommended Actions:
• Review firewall rules
• Validate service necessity
• Confirm asset ownership
This format is intended for:
- Developers
- DevOps teams
- System administrators
- Security reviewers
Not just security specialists.
18. Why This Matters for Developers and Operations Teams
Traditional scan reports are often:
- Too technical
- Too verbose
- Focused on vulnerabilities rather than context
By contrast, this approach aims to:
- Translate technical exposure into clear summaries
- Reduce alert fatigue
- Encourage proactive remediation
- Improve collaboration between security and engineering
The system acts as a bridge between raw scans and human decision-making.
19. Community Feedback Request (Extended)
Feedback is especially welcome on:
- Scan profile design
- Feature correlation ideas
- ML vs rule balance
- Summary quality
- Practical deployment challenges
This concept is intentionally open-ended and exploratory.
20. Data Requirements: Why Scale Matters
A key limitation — and challenge — of this idea is data scale.
Machine learning models cannot be meaningfully trained on a small number of scans. For this concept to work even at a basic level, it would require:
- Thousands of authorized Nmap scan results
- Scans collected across:
- Different network sizes
- Different environments (dev, staging, production)
- Different time periods
- Consistent feature extraction across scans
Without sufficient data volume and diversity, any ML model would:
- Overfit to small patterns
- Produce unreliable risk estimates
- Offer no real advantage over rule-based logic
This article acknowledges that data collection is the hardest part of this proposal.
21. Learning From Patterns, Not From Assumptions
Another important clarification is that the ML component described here is not pre-trained with universal security knowledge.
Instead:
- The model learns patterns from historical scan data
- Risk interpretation improves only as more data is observed
- Early versions would be inaccurate and incomplete
This reinforces the idea that such a system:
- Must evolve gradually
- Requires continuous validation
- Should never operate without human oversight
22. Why This Is a Proposal, Not a Claim
This article does not claim:
- That ML will outperform expert analysts
- That rule-based scanners are insufficient
- That this approach is production-ready
Instead, it proposes a question:
If we already perform thousands of scans, can we extract more contextual value from them over time?
The intention is to open a technical discussion, not to assert superiority.
23. Request for Practical Feedback and Critique
Given the scale and complexity involved, feedback from the community is especially valuable on:
- Whether this idea is realistically feasible
- How scan data could be normalized at scale
- Where ML genuinely adds value vs unnecessary complexity
- How to avoid misleading conclusions from biased data
- Alternative approaches that may achieve similar goals
Critical feedback is not only welcome — it is necessary.
24. Final Clarification
To summarize clearly:
- This is a conceptual defensive idea
- It depends on large-scale, authorized scan data
- It requires careful engineering and validation
- It is not a shortcut or automated security solution
- It is meant to complement existing tools, not replace them
The value of this article lies in discussion, refinement, and collaboration, not in immediate implementation.
25. Proof of Concept: Sample Implementation with Scikit-Learn
To demonstrate the feasibility of this concept, below is a simplified proof-of-concept implementation using Python and scikit-learn. This code illustrates how scan data could be processed and analyzed for defensive risk assessment.
⚠️ Important Notes:
- This is a simplified educational example
- Real implementation would require significantly more data
- All scanning must be authorized
- This code performs analysis only, not scanning
Sample Code: Feature Extraction and Anomaly Detection
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import json
class NetworkScanAnalyzer:
"""
Defensive network scan analyzer using ML for risk assessment.
Analyzes authorized Nmap scan results for anomaly detection.
"""
def __init__(self):
self.scaler = StandardScaler()
self.anomaly_detector = IsolationForest(
contamination=0.1,
random_state=42
)
self.risk_classifier = None
def parse_nmap_results(self, scan_data):
"""
Extract defensive features from Nmap scan results.
Args:
scan_data: Dictionary containing parsed Nmap output
Returns:
DataFrame with extracted features
"""
features = []
for host in scan_data.get('hosts', []):
feature_dict = {
# Basic exposure metrics
'total_open_ports': len(host.get('open_ports', [])),
'total_filtered_ports': len(host.get('filtered_ports', [])),
'total_closed_ports': len(host.get('closed_ports', [])),
# Service diversity
'unique_services': len(set(host.get('services', []))),
'service_version_count': sum(1 for s in host.get('services', [])
if 'version' in s),
# Administrative exposure
'admin_ports_exposed': sum(1 for p in host.get('open_ports', [])
if p in [22, 23, 3389, 5900]),
# Protocol distribution
'tcp_ports': sum(1 for p in host.get('open_ports', [])
if host.get('protocol', {}).get(p) == 'tcp'),
'udp_ports': sum(1 for p in host.get('open_ports', [])
if host.get('protocol', {}).get(p) == 'udp'),
# OS detection confidence
'os_detection_confidence': host.get('os_confidence', 0),
# Temporal features (if historical data available)
'port_change_rate': host.get('port_change_rate', 0),
'new_services_detected': host.get('new_services', 0),
# Risk indicators (rule-based baseline)
'has_deprecated_protocol': int(any(
p in host.get('open_ports', [])
for p in [21, 23, 69, 513, 514]
)),
'has_database_exposed': int(any(
p in host.get('open_ports', [])
for p in [1433, 3306, 5432, 27017]
)),
# Metadata
'host_id': host.get('ip', 'unknown'),
'scan_timestamp': host.get('timestamp', '')
}
features.append(feature_dict)
return pd.DataFrame(features)
def train_anomaly_detector(self, historical_scans):
"""
Train anomaly detection model on historical authorized scans.
Args:
historical_scans: List of parsed scan dictionaries
"""
# Extract features from all historical scans
all_features = []
for scan in historical_scans:
df = self.parse_nmap_results(scan)
all_features.append(df)
# Combine and prepare data
feature_df = pd.concat(all_features, ignore_index=True)
# Select numeric features for ML
numeric_features = feature_df.select_dtypes(
include=[np.number]
).drop(columns=['host_id', 'scan_timestamp'], errors='ignore')
# Normalize features
X_scaled = self.scaler.fit_transform(numeric_features)
# Train anomaly detector
self.anomaly_detector.fit(X_scaled)
print(f"Trained on {len(feature_df)} host scans")
print(f"Feature dimensions: {X_scaled.shape[1]}")
def analyze_scan(self, scan_data):
"""
Analyze a new scan for anomalies and risk assessment.
Args:
scan_data: Dictionary containing parsed Nmap output
Returns:
Analysis results with risk scores and explanations
"""
# Extract features
feature_df = self.parse_nmap_results(scan_data)
# Prepare for prediction
numeric_features = feature_df.select_dtypes(
include=[np.number]
).drop(columns=['host_id', 'scan_timestamp'], errors='ignore')
X_scaled = self.scaler.transform(numeric_features)
# Detect anomalies
anomaly_scores = self.anomaly_detector.decision_function(X_scaled)
is_anomaly = self.anomaly_detector.predict(X_scaled)
# Generate results
results = []
for idx, row in feature_df.iterrows():
result = {
'host_id': row['host_id'],
'anomaly_score': float(anomaly_scores[idx]),
'is_anomalous': bool(is_anomaly[idx] == -1),
'risk_level': self._calculate_risk_level(row, anomaly_scores[idx]),
'observations': self._generate_observations(row),
'recommendations': self._generate_recommendations(row)
}
results.append(result)
return results
def _calculate_risk_level(self, features, anomaly_score):
"""Calculate overall risk level using hybrid approach."""
# Rule-based risk factors
rule_risk = 0
if features['has_deprecated_protocol']:
rule_risk += 30
if features['admin_ports_exposed'] > 0:
rule_risk += 20
if features['has_database_exposed']:
rule_risk += 25
if features['total_open_ports'] > 20:
rule_risk += 15
# ML-based anomaly contribution
# Normalize anomaly score to 0-50 range
anomaly_risk = max(0, min(50, -anomaly_score * 25))
# Combine (weighted average)
total_risk = (rule_risk * 0.6) + (anomaly_risk * 0.4)
if total_risk >= 70:
return "High"
elif total_risk >= 40:
return "Medium"
else:
return "Low"
def _generate_observations(self, features):
"""Generate human-readable observations."""
observations = []
if features['total_open_ports'] > 15:
observations.append(
f"Large attack surface: {features['total_open_ports']} open ports"
)
if features['admin_ports_exposed'] > 0:
observations.append(
f"Administrative services exposed: {features['admin_ports_exposed']} ports"
)
if features['has_deprecated_protocol']:
observations.append("Deprecated protocols detected")
if features['port_change_rate'] > 0.2:
observations.append("Significant configuration changes detected")
if features['new_services_detected'] > 0:
observations.append(
f"New services appeared: {features['new_services_detected']}"
)
return observations
def _generate_recommendations(self, features):
"""Generate defensive recommendations."""
recommendations = []
if features['admin_ports_exposed'] > 0:
recommendations.append("Review firewall rules for administrative ports")
if features['has_deprecated_protocol']:
recommendations.append("Disable deprecated protocols (FTP, Telnet, TFTP)")
if features['has_database_exposed']:
recommendations.append("Verify database ports should be externally accessible")
if features['total_open_ports'] > 20:
recommendations.append("Audit services - consider reducing attack surface")
if features['port_change_rate'] > 0.2:
recommendations.append("Investigate recent configuration changes")
return recommendations
# Example usage
def example_usage():
"""
Demonstration of the defensive scan analyzer.
NOTE: This assumes you have authorized scan data in the proper format.
"""
# Initialize analyzer
analyzer = NetworkScanAnalyzer()
# Example historical scan data (simulated for demonstration)
# In practice, this would come from authorized Nmap scans
historical_scans = [
{
'hosts': [
{
'ip': '192.168.1.10',
'open_ports': [22, 80, 443],
'services': ['ssh', 'http', 'https'],
'protocol': {22: 'tcp', 80: 'tcp', 443: 'tcp'},
'os_confidence': 95,
'timestamp': '2024-01-01',
'port_change_rate': 0.0,
'new_services': 0
}
]
},
# More historical scans would be loaded here...
]
# Train on historical data
print("Training anomaly detector on historical scans...")
analyzer.train_anomaly_detector(historical_scans)
# Analyze new scan
new_scan = {
'hosts': [
{
'ip': '192.168.1.10',
'open_ports': [21, 22, 23, 80, 443, 3306, 3389],
'filtered_ports': [25],
'closed_ports': [],
'services': ['ftp', 'ssh', 'telnet', 'http', 'https', 'mysql', 'rdp'],
'protocol': {21: 'tcp', 22: 'tcp', 23: 'tcp', 80: 'tcp',
443: 'tcp', 3306: 'tcp', 3389: 'tcp'},
'os_confidence': 90,
'timestamp': '2024-01-15',
'port_change_rate': 0.35,
'new_services': 4
}
]
}
print("\nAnalyzing new scan...")
results = analyzer.analyze_scan(new_scan)
# Display results
for result in results:
print(f"\n{'='*60}")
print(f"Host: {result['host_id']}")
print(f"Risk Level: {result['risk_level']}")
print(f"Anomalous: {result['is_anomalous']}")
print(f"Anomaly Score: {result['anomaly_score']:.3f}")
print(f"\nKey Observations:")
for obs in result['observations']:
print(f" • {obs}")
print(f"\nRecommended Actions:")
for rec in result['recommendations']:
print(f" • {rec}")
print(f"{'='*60}")
if __name__ == "__main__":
example_usage()
Key Components Explained
1. Feature Extraction
- Converts raw Nmap output into structured numeric features
- Focuses on defensive metrics (port counts, service diversity, etc.)
- Does not extract exploitation information
2. Hybrid Risk Scoring
- Combines rule-based risk (60%) with ML anomaly detection (40%)
- Rule-based handles known bad patterns
- ML handles environmental context and anomalies
3. Anomaly Detection
- Uses Isolation Forest algorithm
- Learns what is "normal" for the environment
- Flags unusual patterns without needing labeled attack data
4. Actionable Output
- Generates human-readable observations
- Provides defensive recommendations
- Explains risk factors clearly
Limitations of This Example
- Requires thousands of real scans to be effective
- Simplified feature set (production would need more)
- No temporal correlation across multiple scans
- Assumes clean, parsed Nmap data
Next Steps for Production Use
- Collect large dataset of authorized scans
- Expand feature engineering
- Add cross-validation and performance metrics
- Implement scan profile correlation
- Build feedback loop for continuous improvement
- Add extensive logging and audit trails
This code demonstrates that the concept is technically feasible while maintaining a purely defensive focus.
About the Author
@hejhdis
This article is shared in the spirit of collaborative security research. The author welcomes constructive dialogue, criticism, and alternative perspectives from the security community.
This article is intended for educational and research purposes only. All scanning activities discussed assume proper authorization and legal compliance. The concepts presented are exploratory and should not be implemented without thorough security review and ethical consideration.
Top comments (0)