Designing Behavioral Authentication Systems for Infrastructure-Constrained Environments

Modern security architecture is built on abundance.

Abundant telemetry.
Abundant compute.
Abundant global threat intelligence.

Most contemporary SIEM and XDR platforms (ARGOS included) assume a cloud-native environment where logs flow continuously into centralized data lakes and are enriched in real time by external intelligence feeds. Academic research often mirrors this assumption, evaluating detection models on large, labeled datasets with broad contextual visibility.

But many environments do not operate under those conditions.

Air-gapped systems.
Operational technology (OT) networks.
Forensic workstations.
High-security local infrastructures.

In these contexts, the defender does not have global telemetry. There is no real-time IP reputation API. There may not even be sufficient historical data to train large supervised models.

This asymmetry raises a practical question:

How does behavioral authentication detection behave when infrastructure assumptions collapse?

Over the past few months, we explored this question through the development of Chimera, a modular research framework for authentication anomaly detection designed explicitly for constrained environments.

Chimera is not a SIEM. It is not positioned as a production security product. It is a laboratory for studying how unsupervised ensemble models behave when deprived of the cloud.

Infrastructure Asymmetry

Most behavioral detection research implicitly assumes informational symmetry: the defender has broad visibility across networks and access to continuously updated indicators of compromise.

In practice, this symmetry rarely holds in constrained environments.

An attacker can leverage globally available proxies, automation frameworks, and anonymization networks. The defender, operating locally, sees only their own logs. No global correlation. No shared intelligence pool.

We refer to this imbalance as infrastructure asymmetry.

Under asymmetry, common detection techniques degrade in subtle ways. Signals that would be obvious in a globally aggregated dataset become ambiguous when viewed in isolation. The absence of external context forces greater reliance on internal behavioral modeling — which introduces its own limitations.

Data Scarcity and the Limits of Supervision

Deep learning approaches — LSTMs, Transformers, and hybrid graph models — dominate current academic discussions of authentication anomaly detection. These methods require substantial labeled data and long behavioral histories.

Constrained environments rarely have either.

Small or isolated systems may generate only thousands of authentication events per day. Confirmed attack labels are sparse or nonexistent. Transfer learning from global corpora is often infeasible due to policy or connectivity restrictions.

This reality pushes detection toward unsupervised learning: methods such as Isolation Forest and Local Outlier Factor (LOF) that attempt to identify deviations without explicit labels.

However, this introduces a second-order challenge: score reliability.

The Ensemble Reliability Problem

Individual anomaly detectors interpret deviation differently.

• Isolation Forest tends to identify global outliers effectively but may miss subtle density-based shifts.

• LOF captures local density variation but can become unstable in sparse or noisy datasets and is computationally expensive.

The conventional engineering solution is to build an ensemble: combine model outputs and average their scores.

In constrained environments, this approach often fails.

The problem is not conceptual but mathematical. Different algorithms produce scores on incompatible scales and distributions. An Isolation Forest score may lie within a bounded interval, while LOF scores can be unbounded and skewed. Averaging these values without normalization creates distorted composite signals. Strong evidence from one model can be diluted or exaggerated by another.

This issue is rarely discussed explicitly in practitioner literature but becomes pronounced when working with small datasets and limited telemetry.

What Chimera Explores

Chimera v0.2.0 pivots from “running multiple models” to studying ensemble reliability under constraint.

Three core mechanisms were implemented to stabilize signal aggregation:

1. Deterministic Score Normalization

Raw detector outputs are projected onto a consistent [0, 1] interval using training-set-derived MinMax normalization. This enforces comparability across heterogeneous models and ensures that a normalized score reflects relative deviation intensity within each detector’s learned distribution.

The objective is not cosmetic scaling but mathematical coherence.

2. Dynamic Percentile-Based Thresholding

Static thresholds (e.g., “alert if score > 0.7”) are brittle. They generate excessive false positives in noisy systems and silence in low-variance ones.

Chimera derives thresholds dynamically from the ensemble distribution using a contamination percentile approach. Sensitivity adapts to the statistical structure of the observed data rather than relying on fixed constants.

This is particularly important in environments where baseline behavior may shift over time without global calibration.

3. Offline Threat Intelligence as Feature Input

To address infrastructure asymmetry, Chimera integrates static, file-based IP and ASN reputation feeds. These are loaded locally and mapped into memory.

Rather than acting as binary filters, these indicators are introduced as structured features within the ensemble. This allows the model to learn interactions between “known-bad” signals and behavioral deviation without breaking air-gap constraints.

The goal is to enrich, not override, statistical modeling.

What This Does Not Solve

Chimera does not eliminate foundational challenges in behavioral authentication.

• Cold Start: New users lack baseline history. Until sufficient behavioral data accumulates, model outputs remain unstable.

• Contamination Selection: While thresholds are dynamic, the expected anomaly rate still requires domain-informed configuration.

• Ground Truth Absence: Unsupervised systems can suggest deviation but cannot assert malicious intent without additional context.

These limitations are structural to unsupervised modeling and remain open research problems.

Toward Detection Designed for Constraint

The dominant narrative in security engineering emphasizes scale: larger data lakes, broader telemetry aggregation, and deeper neural architectures.

Constrained environments force a different discipline.

They expose hidden assumptions in ensemble logic.
They reveal fragility in naive score aggregation.
They emphasize reproducibility and determinism over opacity.

Chimera is our attempt to study detection systems not under idealized cloud abundance, but under deliberate limitation.

We believe the next generation of behavioral authentication systems must be evaluated not only by how well they scale — but by how gracefully they function when scale is unavailable.

Chimera v0.2.0 is now open-source as a research framework. We invite engineers, researchers, and analysts to examine its assumptions, test its normalization strategy, and challenge its ensemble logic.

Constraint is not an edge case.

For many systems, it is the baseline reality.