Dear Rich,
Thank you again for your thoughtful reading and constructive feedback.
You are absolutely right — this first draft focuses on the strategic and
architectural positioning of OODA-HTTP, with implementation details
intentionally deferred.
We are currently finalizing a dedicated technical section (12.1) that
introduces the semantic vector engine and its temporal memory. This is
not a heavyweight AI module but a lightweight behavioral vectorization
layer. It enables each HTTP request to be interpreted within the OODA
loop (Observe → Orient → Decide → Act), allowing real-time,
context-aware defense.
Key point: HTTP packets only become semantically quantifiable — and
thus defensive — once observed and contextualized. This is the foundation
of active behavior in OODA-HTTP.
This loop enables HTTPS to act rather than just transport.
The more OODA-HTTP is fed, the more defensive it becomes.
We’d be glad to share this section with you before it is integrated into
the next draft, and would highly value your feedback.
Please find attached a technical preview of Section 12.1, detailing the
semantic vector engine and its role in turning HTTP signals into
actionable defense vectors.
Best regards,
Rachid Bouziane
[email protected]
SecRoot.io – OODA-HTTP Protocol Initiative
## Section 12.1 — Semantic Vector Engine and Temporal Memory (Preview)
### Introduction
The Semantic Vector Engine (SVE) in OODA-HTTP is a lightweight behavioral
module that transforms HTTP request metadata into actionable context
vectors. It enables real-time decision-making by converting passive
transport into semantic observations.
### 1. Purpose
OODA-HTTP aims to introduce an active, adaptive layer to HTTP/HTTPS.
The SVE extracts signals from HTTP traffic and feeds them into a local
or distributed decision engine. This section defines the core concepts,
data inputs, and expected behaviors.
### 2. Signal Sources
Each HTTP request may yield multiple behavioral indicators:
* Request frequency (burst, sustained, idle)
* Header entropy (variation in User-Agent, Accept, Referer, etc.)
* Latency and inter-arrival times
* Request origin stability (IP, ASN, TLS fingerprint)
* Payload similarity or uniqueness
These indicators are encoded into low-dimensional semantic vectors.
### 3. Context Vector Format
Vectors follow a lightweight JSON format, e.g.:
```json
{
"request_id": "abc123",
"vector": {
"frequency": 0.89,
"header_variance": 0.66,
"origin_score": 0.91,
"anomaly_index": 0.72
},
"timestamp": "2025-07-01T12:30:45Z"
}
```
### 4. Decision Layer Interface
The output of the vector engine is passed to a local decision policy,
which:
* Compares vector values to learned thresholds or dynamic baselines
* Emits an action (`X-OODA-Action`) when thresholds are crossed
* Logs and feeds into a temporal memory store
### 5. Temporal Memory Role
Temporal memory aggregates past vectors over time to:
* Detect slow-burning or persistent anomalies
* Provide context enrichment for future requests
* Support replay or forensic analysis
### 6. Benefits
* No need for deep packet inspection or heavy AI models
* Enables HTTPS to act on behavior, not just content
* Reduces false positives by contextual scoring
* Fully compatible with TLS/QUIC transport
### 7. Roadmap
This section is a preview. Future drafts will:
* Define vector schemas in IANA-compatible format
* Provide integration samples with NGINX, Envoy, and Node.js
* Standardize the scoring interface for third-party engines
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