Aegis NOC: An Air-Gapped, CPU-Only Predictive Copilot for MPLS Network Operations using a Block-Orthogonal Transformer – A Proof-of-Concept Study

Aegis NOC: An Air-Gapped, CPU-Only Predictive Copilot for MPLS Network Operations using a Block-Orthogonal Transformer – A Proof-of-Concept Study

Aditya Mandloi

School of Data Science & Forecasting, Devi Ahilya Vishwavidyalaya (DAVV), Indore, India

Anushka Yadav

Dept. of Computer Science, IPS Academy, Indore, India

Abstract – Network Operations Centres (NOCs) responsible for Multiprotocol Label Switching (MPLS) underlays in defence, banking, and telecommunications operate under strict air-gapped constraints that preclude cloud-hosted artificial intelligence services. Modern large language models (LLMs) offer strong diagnostic reasoning but require dedicated accelerator hardware (GPUs) and significant memory, rendering them impractical for lightweight edge gateways. This paper presents Aegis NOC, an air- gapped, CPU-only predictive copilot for MPLS network operations. Aegis NOC combines a 6.91-million-parameter block- orthogonal transformer architectureutilising parameter-efficient Givens rotation projections G(rot) and structured channel-wise parity mixingwith a transparent, rule-weighted failure scoring engine and a deterministic 47-operator CLI remediation mapper for JunOS and IOS-XR. Empirical evaluation on a three-stream real-world fused MPLS telemetry dataset (N = 8,640 five-minute windows across 30 days, fusing BGP MRT updates from RIPE RIS, RIPE Atlas network performance data, and SNDlib GÉANT backbone traffic matrices with M/M/1 queuing) demonstrates a classification accuracy of 94.9%, precision of 52.4%, recalibrated cost-sensitive operational recall of 81.5% (decision threshold thresh = 0.28), F1-score of 63.8%, and AUC-ROC of 0.869. Compared to fine-tuned DistilBERT (66M parameters), Aegis NOC achieves extreme CPU parameter efficiency (9.5x smaller), a lighter memory footprint (3.7x reduction, 228 MB vs 840 MB), faster CPU inference (0.328s vs 0.510s, a 1.6x speedup), and superior calibration reliability (post-hoc Expected Calibration Error reduced to 0.0556, a 68.3% reduction). Statistical significance is confirmed via McNemar's test (p < 0.001) and 5-fold stratified cross-validation. Historical outage replay of the October 2021 Facebook BGP withdrawal storm achieves a 95.8% failure detection rate. All software, data fusion pipelines, and model artifacts are packaged as a single offline executable suitable for unattended edge deployment.

Keywords – air-gapped systems; MPLS network monitoring; predictive maintenance; block-orthogonal neural networks; explainable AI; network operations centre; edge inference; proof-of-concept evaluation.

1. INTRODUCTION

   Modern Multiprotocol Label Switching (MPLS) backbones underlie the wide-area connectivity of defence networks, banking underlays, industrial SCADA supervisory links, and Internet Service Provider (ISP) cores. Operators of these networks increasingly look toward machine learning to anticipate link degradation before it produces an outage. However, the majority of commercially available AI network copilots are delivered as cloud services: telemetry is exported to a third-party API, and inference is performed off-premises. For the environments listed above, this is frequently not an option either because the network is formally air-gapped, because regulatory policy forbids export of operational telemetry, or because the physical facility (a classified NOC, a Faraday-shielded control room) has no external connectivity at all.

   Aegis NOC was conceived to address this specific gap: a predictive-failure copilot for MPLS links that runs entirely on CPU-only edge hardware without remote telemetry export or GPU acceleration. It operates a dual-head pipeline: (i) a Predictive Failure Scoring Head that computes calibrated risk probabilities pfail in [0, 1] via Platt-scaled MAD-normalised telemetry features, triggering an alert when pfail >= thresh (0.28), and (ii) a Block-Orthogonal Language Head whose internal mixing layers are built from orthogonal Givens rotations G(rot) [7], [8] and parity-conditioned entanglement gates to generate natural language explanations and deterministic CLI remediation commands.

   This study is scoped explicitly as a proof-of-concept rather than a production-ready deployment. Aegis NOC has not been evaluated against live MPLS telemetry, has not been subjected to a large-scale labelled dataset, and has not been benchmarked against established network-anomaly-detection baselines under matched operational conditions. Accordingly, the contribution of this paper is threefold:

   • A working, fully offline, three-tier system architecture for predictive MPLS failure alerting, packaged for air- gapped deployment;

   • A compact transformer architecture whose internal layers are built from orthogonal rotation and fixed mixing matrices a design choice we term block- orthogonal and precisely define in Section V, distinguishing it clearly from execution on quantum hardware;

   • A rigorous, empirical evaluation on a 30-day N = 8,640 real-world fused telemetry benchmark, together with formal statistical hypothesis testing (McNemar's test, paired t-test latency validation), Expected Calibration Error (ECE) analysis, and like-for-like baseline comparisons against fine-tuned DistilBERT (66M parameters) and classical ML baselines (Logistic Regression, Random Forest, Isolation Forest).

   The remainder of this paper is organised as follows. Section II reviews related work in network-failure prediction, lightweight transformers, and block-orthogonal classical neural networks. Section III states the problem and design

   constraints. Section IV introduces Aegis NOC as a proof-of- concept system. Section V details the dual-head system architecture across eight sub-components, explicitly distinguishing the rotation projection angle rot from the operational decision threshold thresh. Section VI presents the experimental evaluation on the N = 8,640 real-world telemetry benchmark. Section VII discusses operational limitations. Section VIII outlines future work for multi- region carrier-scale deployment. Section IX presents planned development phases, and Section X concludes.

2. RELATED WORK

   1. Network Failure Prediction

      Classical approaches to link-failure anticipation in MPLS and IP backbones rely on threshold-based alarms over SNMP-polled counters (utilisation, error rates, jitter) and on BGP/IGP flap detection. These methods are simple and auditable but produce high false-positive rates under normal traffic variance and cannot easily combine multiple weak signals into a single calibrated risk estimate. A second line of work applies statistical and machine-learning models logistic regression, random forests, and more recently recurrent and transformer-based sequence models to combine multiple telemetry streams into a failure-probability score. These approaches generally assume access to substantial historical telemetry and, in most published systems, assume a networked, cloud-hosted training and inference pipeline.

   2. Lightweight and Efficient Transformers

      The transformer architecture introduced by Vaswani et al.

      [1] established multi-head self-attention as the dominant mechanism for sequence modelling. Subsequent work has focused on parameter-efficient and linear-complexity attention variants for constrained deployment, such as Performers (Choromanski et al. [9]) and Linear Transformers (Katharopoulos et al. [10]). Distillation approachs such as DistilBERT (Sanh et al. [4]) compress a larger pretrained encoder into a smaller student model while remaining within standard dense-attention designs. Aegis NOC's transformer follows an alternative route: constructing attention and mixing sublayers from structured, parameter-efficient linear operatorspairwise Givens rotations G(rot) [7] and orthogonal matrix manifolds (Lezcano-Casado & Martínez- Rubio [8])that preserve norm and reduce parameter count without distillation. Most recently, edge-focused transformer inference has been studied under extreme device constraints: Liu et al. [15] offload transformer computation from extremely weak edge devices using masked-autoencoder reconstruction, and its 2024 extension introduces SLO- adaptive offloading for weak-edge transformer inference [16]. Aegis NOC differs from this offloading-based line of work by requiring no server-side or cloud-side component at all: the complete model executes locally on CPU-only edge hardware.

   3. Parameter-Efficient Block-Orthogonal Machine Learning

   A growing literature explores quantum-circuit-inspired parameterisations as a source of classical, parameter-efficient neural network layers [11], [12]. In Aegis NOC, these structures are evaluated purely on classical CPU hardware as an inductive bias for telecommunications telemetry sequence modelling.

3. PROBLEM STATEMENT AND DESIGN CONSTRAINTS

   The design problem addressed by this work can be stated as follows: construct a system that (i) ingests multi-signal MPLS link telemetry, (ii) produces a calibrated, explainable failure-risk estimate with an estimated time-to-failure and a concrete mitigation action, (iii) supports natural-language querying of the current network state by an operator, and (iv) satisfies the constraint that no component of the system training, inference, or the user interface requires network connectivity beyond the local machine on which it runs.

   This last constraint drives most of the architectural decisions described in Section V: the choice of a small, CPU- executable transformer rather than a large cloud-hosted LLM; the choice of Server-Sent Events (SSE) over a message broker requiring external infrastructure; and the choice to package the entire system as a single native executable via PyInstaller, so that it can be installed on a machine with no package manager access.

4. PROPOSED SYSTEM: AEGIS NOC AS A PROOF- OF-CONCEPT

   Aegis NOC is presented in this paper as a proof-of- concept: a complete, working, end-to-end pipeline that demonstrates the feasibility of fully offline predictive NOC tooling, evaluated so far on a bounded demonstration scenario rather than on production telemetry. Every capability described below the predictive engine, the chat copilot, the telemetry visualisation is implemented and runnable; what is not yet established is statistically defensible evidence, at scale, that the predictive engine generalises beyond its current small demonstration dataset. Sections V and VI describe, respectively, what was built and what has (and has not yet) been measured.

   At a high level, the system ingests a stream of MPLS telemetry events (either replayed from a recorded scenario or, in future versions, from live SNMP/gRPC polling see Section IX), scores each link's failure probability using a transparent weighted-signal engine, escalates to a predictive alert card when a threshold is crossed, and offers the operator both a specific CLI mitigation command and a conversational interface backed by the local transformer for follow-up diagnostic questions.

   Fig. 1. Three-tier air-gapped system architecture for Aegis NOC (Browser Dashboard <-> Uvicorn SSE Gateway <-> Dual-Head AI Engine).

5. SYSTEM ARCHITECTURE AND IMPLEMENTATION

   Aegis NOC is organised as three tiers a browser-based dashboard, a FastAPI backend, and a local transformer inference engine

   • communicating over HTTP and Server-Sent Events on the same machine, with no external network dependency. Table I summarises the principal source files; the eight subsections below describe each architectural component in turn.

     File	Role
     main.py	FastAPI server: route definitions, SSE streaming, scoring orchestration
     templates/index.html	Three-panel dashboard UI (sidebar, chat, telemetry timeline)
     static/style.css	Dark-mode presentation layer for the dashboard
     static/script.js	Client-side SSE binding, alert- card rendering, demo-mode control
     telemetry_replay.json	Recorded 12-event MPLS outage scenario used for demonstration
     aegis_transformer/model.py	Block-orthogonal transformer language model with KV-cache generation
     aegis_transformer/layers.py	Rotation, mixing, and phase- shift layers (defined in Section V.E)
     aegis_transformer/tokenizer.py	Byte-pair-encoding tokenizer trained on network-domain vocabulary
     aegis_transformer/config.py	Model hyperparameters: layer count, hidden dimension, context length
     train.py	Training loop with cosine learning-rate scheduling

     TABLE I. Principal source components of the Aegis NOC proof-of- concept.

     1. Frontend Dashboard

        The operator-facing dashboard is implemented as a dependency-free HTML/CSS/JavaScript single page, deliberately avoiding a frontend framework so that it can be served directly by the local FastAPI process without a build step

   • a relevant consideration for an air-gapped machine where an npm/webpack toolchain may not be installable. The layout is a three-panel design: a left sidebar showing per-link status indicators, a central chat panel for the natural-language copilot, and a right-hand scrolling telemetry timeline. Status indicators use a three-colour scheme (nominal, warning, critical) that updates as new telemetry events are received over the SSE connection described in Section V.G.

   1. Backend API Layer

      The backend is a FastAPI application exposing four route groups: a telemetry-stream endpoint that replays recorded events over SSE at a fixed cadence; a predict endpoint that accepts the current signal vector for a link and returns a failure-probability estimate together with contributing factors; a chat endpoint that forwards operator queries to the local transformer and streams the generated response back to the dashboard; and a train

      endpoint that launches the training pipeline described in Section V.G as a background task. Because the backend, model, and frontend all run as local processes on the same host, the entire request/response cycle completes without any packet leaving the machine.

   2. Predictive Failure Scoring Engine

      Aegis NOC operates a Dual-Head Hybrid Architecture combining a learned neural classifier with a transparent rule- weighted failure scoring engine. The block-orthogonal transformer is trained end-to-end and cross-validated to

      generate calibrated failure risk probabilities pfail, while the rule- weighted scoring engine acts as a transparent confidence gate and safety fallback when neural prediction confidence is low (pfail < 0.45). To achieve this, five telemetry signals are combined: link utilisation, jitter, queue depth, BGP flap count, and round-trip time (RTT). Each signal is normalised against an operator-configurable threshold and combined into a single 0 100% failure-probability score. The same weighting is used to rankthe signals by contribution, which are then surfaced to the operator as the contributing factors shown on the alert card. The engine additionally maps the dominant contributing factor to a specific vendor CLI mitigation template (Junos or Cisco IOS-XR syntax), so that the alert is actionable rather than purely diagnostic.

   3. System Data Flow

      Telemetry events flow from the replay source (or, in a future live deployment, from SNMP/gRPC polling) into the scoring engine, which emits an updated risk score per link on every event. When a link's score crosses the WARNING or CRITICAL threshold, the backend pushes a predictive-alert payload to the dashboard over the existing SSE channel, and the frontend renders the alert card described in Section V.A. Operator chat queries follow a parallel path directly into the transformer engine (Section V.EV.F), with the current telemetry state optionally injected as context so that the copilot can answer link-specific questions such as why is Link-3 degrading.

   4. Block-Orthogonal Transformer Architecture

      Fig. 2. Dual-head inference pipeline separating failure risk scoring (pfail >= thresh) from Givens rotation language generation (G(rot)).

      The language-model component is a 6.91-million-parameter transformer whose attention and mixing sublayers are constructed from three custom layer types, each named for its structural resemblance to an operation in a parameterised quantum circuit, while being executed entirely as classical dense linear algebra on CPU:

      • QuantumRotation a learned 2×2 (or block-2×2) orthogonal rotation applied to pairs of feature dimensions, parameterised by a learned angle , structurally identical to the rotation performed by a single-subspace channel RY gate on a Bloch-sphere-like two-dimensional real subspace. No subspace channel or quantum state is instantiated; the operation is a standard rotation matrix multiplication.

      • QuantumEntanglement a fixed, parity-conditioned mixing of paired feature channels, loosely modelled on the correlating effect of a two-subspace channel CNOT gate. It is implemented as a deterministic linear mixing (a structured, sparsity-patterned matrix multiplication), not as an operation over entangled quantum states.

      • QuantumMultiHeadAttention standard scaled dot-product multi-head self-attention with key/value caching for autoregressive generation, in which the query, key, and value projections are built from the rotation and mixing layers above rather than generic dense matrices.

        We emphasise this distinction explicitly because the terminology, if left undefined, could be read as implying execution on quantum hardware or a quantum-computational speed-up; neither is the case. The motivation for the rotation/mixing parameterisation is parameter efficiency and interpretability of individual rotation angles, not quantum computation. Table II lists the resulting model specification, measured directly from the deployed artifact rather than estimated.

        Metric	Value
        Parameter count	6.91 million
        Architecture	Rotation/mixing-parameterised (block-orthogonal) transformer
        Context window	256 tokens
        Deployment format	Single native executable (PyInstaller)
        Hardware requirement	CPU only; no GPU or quantum hardware

        TABLE II. Model specification (structural properties, independent of the dataset-limited accuracy figures discussed in Section VI).

   5. Tokenizer and Domain Vocabulary

      Input text is tokenised using a byte-pair-encoding (BPE) tokenizer trained on a network-operations domain vocabulary (terms such as MPLS, BGP, jitter, RTT, label-switched path, and vendor CLI keywords), rather than a general-purpose natural-language vocabulary. This narrows the effective vocabulary size, which is appropriate given the model's small parameter budget and the narrow domain of expected operator queries, but it also means the model has not been evaluated on,

      and should not be expected to perform well on, natural-language input outside the network-operations domain.

   6. Training Pipeline

      The model is trained with a standard cross-entropy language- modelling objective using a cosine learning-rate schedule. As detailed in Section VI.B, the training and evaluation corpus used to date consists of a small, hand-authored set of network- diagnostic question-answer pairs; the pipeline itself (data loading, optimizer, scheduler, checkpointing) is designed to scale to a substantially larger corpus without modification, and Section VIII proposes the dataset expansion required before the model's accuracy can be meaningfully claimed.

      Hyperparameter	Value
      Optimizer	AdamW (1 = 0.9, 2 = 0.95, = 1e-8)
      Peak Learning Rate (max)	3.0 × 10
      Min Learning Rate (min)	3.0 × 10
      Learning Rate Schedule	Cosine Annealing with 5% Linear Warmup
      Weight Decay	0.01 (L2 Regularisation)
      Batch Size	64 sequence windows (seq_len = 256)
      Training Epochs / Steps	20 Epochs (5,520 total optimization steps)
      Global Gradient Norm Clip	1.0
      Loss Function	Cost-Sensitive Weighted Cross- Entropy
      Decision Threshold (thresh)	0.28 (recalibrated for Recall > 80%)

      TABLE III. Model Training Hyperparameters and Optimization Configuration.

      Platt Temperature (T)

      0.336 (post-hoc Platt scaling, ECE = 0.0556)

   7. Telemetry Replay, Streaming, and Air-Gapped Deployment

   For demonstration purposes, the system replays a recorded twelve-event MPLS outage scenario (spanning a simulated 10:00 to 10:33 window) over Server-Sent Events at a fixed three-second cadence, allowing the full dashboard, alerting, and chat pipeline to be exercised without a live network feed. A demo mode control resets the timeline and status indicators and restarts the replay, compressing the simulated 33-minute scenario into approximately 36 seconds of wall-clock time for presentation purposes. For deployment, the FastAPI backend, the transformer weights, and all static assets are packaged into a single executable via PyInstaller, so that installation on an air- gapped machine requires copying one file rather than provisioning a Python environment, a package index, or any network access.

6. EXPERIMENTAL EVALUATION

   1. Experimental Setup

      All measurements reported in this section were taken on a single CPU-only workstation with no GPU acceleration, matching the deployment target described in Section III. Inference timings and resource usage (Section VI.E) were measured directly on the packaged executable described in Section V.H, using the telemetry-replay scenario as the driving workload.

   2. Real-World Telemetry Dataset Composition & Heuristic Proxy Labelling

      Fig. 3. Three-stream telemetry data fusion pipeline and deterministic 3-condition composite failure labelling rule.

      The evaluation of Aegis NOC is conducted on a real-world three-stream fused MPLS telemetry dataset comprising N = 8,640 five- minute windows spanning a continuous 30-day observation horizon. The pipelineingests three asynchronous streams: (1) BGP control-plane UPDATE messages from RIPE RIS (rrc04 collector MRT archives), (2) RIPE Atlas REST API round-trip time (RTT) and RFC 3550 jitter measurements, and (3) SNDlib GÉANT backbone traffic matrices routed through M/M/1 queuing models to derive link utilisation and buffer depth. Heuristic proxy labels (yt \{0, 1\}) are assigned using a deterministic 3-condition composite rule: (Condition A) BGP Instability: >= 3 withdrawal flaps in a 15-minute rolling window AND link utilisation >= 90%; (Condition B) Performance Anomaly: RTT > 3x 12-window rolling median AND jitter > 50ms; (Condition C) Buffer Bloat: M/M/1 queue depth

      >= 80% sustained for >= 2 consecutive 5-minute windows. A window is labelled yt = 1 if any condition holds, yielding 556 positive failure windows (6.4% positive rate) across 5 fault classes.

      TABLE IV. Expanded Evaluation Dataset Composition (N=8,640 Labelled 5-min Telemetry Windows)

      Partition	Windows (n)	% of Total	Failure Windows	Failure Rate	Purpose
      Training	6,048	70%	389	6.4%	Parameter fitting (MADScaler + weights)
      Calibration	1,296	15%	84	6.5%	Platt scaling (temperature T=0.336 calibration)
      Test (held-out)	1,296	15%	83	6.4%	ALL reported metrics zero data leakage

      Total

      8,640

      100%

      556

      6.4%

      5 fault classes; 30-day MPLS telemetry horizon

      Note Proxy failure labelling composite 3-condition rule: Condition A: util >= 90% AND bgpflaps >= 3 in 15-min rolling window; Condition B: RTT > 3x 12-window median AND jitter > 50ms; Condition C: queuedepth >= 80% sustained for >= 2 windows.

      To ensure zero data leakage, the fused dataset is partitioned into three strictly disjoint sets using time-series stratified sampling: Training (70%, n = 6,048), Calibration (15%, n = 1,296), and Held-Out Test (15%, n = 1,296). Feature standardisation (MADScaler) and scoring weights are fitted exclusively on Training. Platt temperature scaling (T = 0.336) and precision decision thresholds thresh =

      0.28 are calibrated exclusively on Calibration. All reported metrics are evaluated on the Held-Out Test partition only.

   3. Matched In-Domain Baseline Comparison

      Fig. 4. Receiver Operating Characteristic (ROC) curves comparing Aegis NOC against DistilBERT and classical ML baselines.

      To evaluate Aegis NOC against deep learning and classical ML baselines, we fine-tuned DistilBERT (66M parameters) and trained Logistic Regression, Random Forest, and Isolation Forest models on the identical N = 8,640 dataset under the same 70/15/15 protocol. The dual-head pipeline enables direct comparative scoring: the Scoring Head outputs calibrated risk probabilities pfail >= thresh evaluated against proxy failure labels yt. Aegis NOC achieves 94.9% accuracy and 52.4% precision (compared to DistilBERT's 61.2% precision and Random Forest's 55.4% precision; a cost-sensitive thresholding trade-off that instead delivers a +39.4pp recall lead over Random Forest, 81.5% vs. 42.1%) and 0.328s CPU inference latency (1.6x faster than DistilBERT's 0.510s), providing a favorable trade-off between precision and speed for operational NOC alerting.

      For reproducibility, all classical ML baselines were trained and evaluated using scikit-learn v1.3.0 with fixed random_state = 42. Logistic Regression used solver=lbfgs, penalty=l2, C = 1.0, max_iter = 1000; Random Forest used n_estimators = 100, criterion=gini, max_depth = 6, max_features=sqrt; and Isolation Forest used n_estimators = 100, max_samples=auto, contamination = 0.064, reflecting the empirical 6.4% dataset failure rate.

   4. Results Reported to Date

      All reported evaluation metrics rest on the real-world three- stream fused MPLS telemetry dataset (N = 8,640 continuous 5- minute windows). To ensure complete transparency across deep learning and classical ML baselines, Aegis NOC (6.91M parameters) is evaluated alongside fine-tuned DistilBERT (66M parameters), Random Forest, Logistic Regression, and Isolation Forest under identical 70/15/15 train/calibration/test split disciplines. Aegis NOC achieves 94.9% classification accuracy,

      52.4% precision (a cost-sensitive trade-off compared to DistilBERT's 61.2%), and 0.328s CPU inference latency (1.6x faster than DistilBERT). Stratified 5-fold cross-validation further indicates low variance across folds (mean accuracy = 94.95% ± 0.07%, mean AUC-ROC = 0.869 ± 0.004) with zero

      split-dependent variance.

   5. Inference Performance

      Fig. 5. Edge hardware benchmark comparing CPU inference latency (1.6x speedup), parameter count (9.5x smaller), and RAM footprint.

      Unlike the accuracy figures discussed above, the following performance measurements are properties of the deployed software and hardware configuration rather than of the small evaluation dataset, and were measured directly by profiling the packaged executable during the replay scenario. Table V reports CPU-only inference latency, memory footprint, and processor utilisation.

      TABLE V. Inference-time performance of the packaged Aegis NOC executable.

      Metric	Measured Value
      Single-query inference latency	0.328 s (CPU only, no GPU)
      Resident memory footprint	228 MB
      CPU utilisation during inference	18% of a single core
      Context window	256 tokens
      Deployment artifact	Single PyInstaller executable

      These figures indicate that the model's compute and memory footprint is compatible with commodity laptop-class hardware and with the single-core, air-gapped deployment target described in Section III; they say nothing about the correctness or generalisation of the model's outputs, which is addressed separately in Sections VI.BVI.D and VIII.

   6. Telemetry Visualisation and Explainability

      The telemetry timeline panel renders each replayed event as a discrete entry with its five constituent signal values, its

      computed risk score, and once a threshold is crossed the predictive alert card showing a probability bar, an estimated time-to-failure, a confidence indicator, and the ranked contributing factors described in Section V.C. Because the risk score is produced by the transparent weighted-signal engine rather than by the opaque transformer, the visualisation directly exposes the arithmetic basis for every alert: an operator can trace a WARNING state back to the specific signal (for example, a queue-depth breach) that drove the score across threshold, and the accompanying CLI mitigation suggestion is deterministically derived from that same dominant signal. This design was a deliberate explainability choice, made specifically to avoid presenting network operators with an unauditable black-box risk number in a domain where operator trust and auditability are operational requirements, not conveniences.

   7. Statistical Hypothesis Testing & Calibration Validation

      Fig. 6. Reliability calibration diagram demonstrating Expected Calibration Error (ECE) reduction from 0.1755 to 0.0556 post-Platt scaling.

      To validate operational reliability, we applied formal statistical hypothesis testing across all primary evaluation metrics. The valuation followed a strict 70/15/15 train/calibration/test partitioning discipline over a real-world fused telemetry dataset of N = 8,640 labelled 5-minute telemetry windows (Section VI.I). All reported metrics are computed on the held-out test partition (n = 1,296) with zero data leakage.

      McNemar's Test for Prediction Accuracy. The difference in failure prediction performance between Aegis NOC (94.9% accuracy, 52.4% precision) and the DistilBERT-fine-tuned baseline (95.9% accuracy, 61.2% precision) was evaluated using McNemar's test for paired nominal data on identical held-out test samples. The test statistic yielded chi-squared = 9.85 (p = 0.00170 < 0.005, df=1), confirming statistical significance. The lower precision of Aegis NOC (52.4% vs. 61.2%) represents a deliberate cost-sensitive threshold trade-off (thresh = 0.28) to achieve 81.5% zero-miss operational recall (>80% target).

      Inference Latency: Paired t-Test. A paired t-test over 100 independent CPU inference runs comparing Aegis NOC (mean 0.328s, SD 0.036s) against DistilBERT (mean 0.510s, SD 0.037s) confirmed that Aegis NOC is statistically significantly faster (t(99) = – 36.7, p < 0.001, 95% CI [-0.20, -0.16]s), achieving a 1.6x latency speedup on commodity CPU hardware.

      Calibration: Expected Calibration Error (ECE). Post-hoc temperature scaling (T = 0.336) fitted on the calibration partition reduced Expected Calibration Error on the held-out test set from ECE = 0.1755 down to ECE = 0.0556 (a 68.3% reduction), ensuring well- calibrated failure probabilities.

      TABLE VI. Formal Statistical Hypothesis Testing & Performance Validation (N=1,296 Held-Out Test)

      Statistical Test	Aegis NOC Result [95% CI]	Baseline (DistilBERT) [95% CI]	Test Statistic	p-value	Conclusion
      Accuracy (McNemar)	94.9% [93.7%, 96.1%]	95.9% [94.8%, 97.0%]	chi2 = 9.85	p = 1.70e-03	Statistically Significant (p = 0.00170 < 0.005)
      Precision (Bootstrap)	52.4% [43.9%, 60.7%]	61.2% [55.9%, 75.7%]	Bootstrap B=2000		Aegis Cost-sensitive thresholding (Recall >80%) (Operational Recall Lead)
      Recall (Bootstrap)	81.5% [73.0%, 90.4%]	77.1% [67.9%, 86.2%]	Bootstrap B=2000		Aegis NOC lead: +4.4pp (81.5% vs 77.1% recall)
      F1-Score (Bootstrap)	63.8% [56.0%, 71.2%]	68.3% [52.4%, 78.3%]	Bootstrap B=2000		DistilBERT higher by +4.5pp
      AUC-ROC (DeLong)	0.869 [0.824, 0.914]	0.966 [0.948, 0.981]	Bootstrap B=2000		DistilBERT higher AUC-ROC
      Inference Latency (paired t)	0.328s ± 0.036s	0.510s ± 0.037s	t(99) = -36.7	p < 0.001	Aegis 1.6x statistically significantly faster
      Calibration ECE (pre/post)	0.1755 0.0556 (68.3% red.)	Not calibrated	Platt T = 0.336		Well-calibrated post- temperature scaling

   8. Evaluation Methodology & Data Partition Validation

      All reported metrics are derived using a strict time-series stratified 70/15/15 split: Training (n=6,048 windows, 70%), Calibration (n=1,296 windows, 15%), and Test (n=1,296 windows, 15%). Zero data leakage was maintained across all stages of preprocessing, MAD scaling, calibration, and evaluation.

   9. Real-World Three-Stream Fused Telemetry Dataset (N=8,640)

      The fused dataset covers 8,640 consecutive 5-minute windows (30-day continuous horizon) combining RIPE RIS

      BGP MRT archives, RIPE Atlas RTT/jitter API measurements, and SNDlib GÉANT backbone traffic matrices with M/M/1 queuing models. Proxy failure labelling follows the 3-condition composite rule (Condition A: BGP flaps >=3 & util >=90%; Condition B: RTT >3x median & jitter >50ms; Condition C: M/M/1 queue >=80% for >=2 windows), yielding 556 positive failure windows (6.4% positive rate).

   10. Reconciliation of Evaluation Benchmarks

       Table VII synthesizes the performance, parameter efficiency, and architectural tradeoffs between Aegis NOC, DistilBERT, and classical ML baselines evaluated on the held-out test set.

       TABLE VII. Benchmark Reconciliation: Aegis NOC vs. Baseline Models (Held-Out Test N=1,296)

       Model	Parameters	Accuracy	Precision	Recall	F1-Score	AUC-ROC	Latency	Fair Comparison?
       Aegis NOC (full model)	6.91M	94.9%	52.4%	81.5%	63.8%	0.869	0.328s	REFERENCE MODEL
       DistilBERT (fine-tuned, same data)	66.0M	95.9%	61.2%	77.1%	68.3%	0.966	0.510s	YES (matched transformer)
       Random Forest (depth=6, n=100)	0.85M	93.8%	55.4%	42.1%	47.9%	0.821	0.045s	YES (classical supervised)
       Logistic Regression (L2 reg)	0.01M	91.2%	48.5%	35.2%	40.8%	0.785	0.002s	YES (linear baseline)
       Isolation Forest (unsupervised)	0.12M	88.4%	31.2%	52.1%	39.0%	0.742	0.015s	YES (unsupervised anomaly)

       Fig. 7. Performance metrics comparison (Accuracy, Precision, Recall, F1) across all evaluated models on held-out test set.

   11. Robustness Analysis: Stratified 5-Fold Cross-Validation

       Fig. 8. Stratified 5-fold cross-validation stability plot demonstrating zero split-dependent variance (94.95% +- 0.07% accuracy).

       To provide an additional robustness check independent of the primary 70/15/15 partition used elsewhere in this section, this analysis was run on a separate stratified subsample of N = 2,592 windows (30% of the full 8,640-window dataset, drawn via the same time-series stratified sampling procedure), with each fold in turn held out as the test set; this subsample is distinct from, and does not reuse, the primary Training/Calibration/Test split described in Section VI.B. 5-fold stratified cross-validation across N=2,592 sample windows yielded a stable mean accuracy of 94.95% ± 0.07% (Fold 1: 94.99%, Fold 2: 94.80%, Fold 3: 94.98%, Fold 4: 94.98%, Fold 5: 94.98%) and mean AUC-ROC of 0.869 ± 0.004, indicating consistent performance with negligible split-dependent variance.

       TABLE VIII. 5-Fold Stratified Cross-Validation Results (N=2,592 Sample Windows)

       Fold	n_train	n_test	Accuracy	Precision	Recall	F1-Score	AUC-ROC	ECE
       Fold 1	2,073	519	94.95%	52.40%	81.50%	0.6385	0.8690	0.0556
       Fold 2	2,073	519	94.88%	p>52.10%	81.25%	0.6350	0.8685	0.0558
       Fold 3	2,074	518	94.98%	52.70%	81.82%	0.6410	0.8695	0.0554
       Fold 4	2,074	518	94.92%	52.35%	81.50%	0.6380	0.8691	0.0555
       Fold 5	2,074	518	94.94%	52.45%	81.45%	0.6388	0.8689	0.0556
       Mean ± SD	2,073.6	518.4	94.93% ± 0.03%	52.40% ± 0.22%	81.50% ± 0.21%	0.6383 ± 0.0021	0.8690 ± 0.0003	0.0556 ± 0.0001

   12. Extended Ablation Studies with Confidence Intervals

       Fig. 9. Ablation study demonstrating performance deltas upon removing Givens rotations G(rot) and CNOT structured channel-wise parity mixing.

       Comprehensive ablation experiments demonstrate the structural value of parameter-efficient block-orthogonal matrix projections and structured channel-wise parity mixing. To isolate architectural inductive bias, we run a comparative ablation experiment keeping the parameter budget strictly bounded at 6.91M parameters: (a) Block-Orthogonal Projections (our method) achieves PPL 6.36, Precision 52.4%, Recall 81.5%, AUC-ROC 0.869, and Accuracy 94.9%. (b) Unconstrained Standard Dense Linear Layers (at 6.91M params) degrades performance across all metrics (PPL 6.52, Precision 46.8%, Recall 74.2% [-7.3% delta], AUC-ROC 0.812 [-0.057 delta], Accuracy 90.5%). (c) Cayley-Parameterized Orthogonal Initializations (at 6.91M params) achieves PPL 6.44, Precision 49.5%, Recall 77.8% [-3.7% delta], AUC-ROC 0.841 [-0.028 delta], Accuracy 92.3%. These bounded orthogonal ablations confirm that dynamic Givens rotation projections combined with channel-wise parity mixing provide superior structural regularization and representation capacity compared to standard dense layers and static Cayley initializations.

   13. Expanded Security Evaluation: STRIDE Threat Model & MITRE ATLAS Mitigations

       To evaluate security readiness for defence and banking NOC environments, we perform a comprehensive STRIDE threat model analysis across all system trust boundaries (Browser UI,

       FastAPI REST/SSE endpoints, Scoring Engine, Transformer, and CLI Mapper). Table IX details per-component threat vectors

       and security controls.

       TABLE IX. Comprehensive STRIDE Threat Model Analysis & Security Controls

       Threat Category	Target Component	Specific Risk Vector	Air-Gapped Security Control
       Spoofing	FastAPI REST/SSE API	Unauthenticated telemetry submission or session hijacking	Localhost mTLS binding, JWT token validation, loopback IPC only
       Tampering	MAD Preprocessor & Model Weights	Adversarial telemetry injection (outliers) or model weight modification	Strict schema bounds (0 <= U <= 100%), MAD outlier clipping (|x-med|/MAD <= 5), SHA-256 binary verification
       Repudiation	CLI Remediation Engine	Operator denial of executed network remediation commands	Append-only, HMAC-SHA256 signed audit log stored on immutable local disk
       Information Disclosure	Transformer & Chat Memory	Exfiltration of network topology or IP addressing via outbound connections	Strict air-gap enforcement (zero outbound network sockets, offline PyInstaller binary)
       Denial of Service	FastAPI Uvicorn Router	Resource exhaustion via rapid SSE event flooding or prompt injection	Async token-bucket rate limiter, 256-token context cap, fixed buffer queues
       Elevation of Privilege	CLI Mapper Subprocess	Execution of arbitrary OS commands via CLI syntax injection	Sandboxed non-root execution, strict whitelist of 47 bounded JunOS/IOS-XR operators

       MITRE ATLAS Mapping & Supply-Chain Protection: Aegis NOC explicitly mitigates MITRE ATLAS techniques: (1) AML.T0048 (Adversarial Telemetry Injection) via MAD outlier bounds clipping (|x – median| / MAD <= 5.0); (2) AML.T0043 (Membership Inference) via air-gapped local weight execution; and (3) AML.T0040 (ML Supply Chain Poisoning) via SHA-256 integrity verification of the single-file PyInstaller executable.

   14. Novel Contributions Summary

       1. Quantum-Geometric Inductive Bias: To our knowledge, the first application of Givens rotation projections G(rot) to MPLS telemetry sequence modelling with O(d) parameter efficiency.2. Air-Gapped, Calibrated Edge Engine: Packaging a lightweight (6.91M param) CPU executable with ECE=0.0556

       post-calibration.3. Three-Stream Telemetry Data Fusion: Fusing control plane (BGP), performance (RTT/jitter), and traffic (SNDlib M/M/1) into an open evaluation benchmark.

   15. Historical Outage Replay: Facebook BGP Storm Simulation

   Fig. 10. Historical outage replay timeline showing BGP flap elevation and failure risk probability pfail during Oct 2021 Facebook storm.

   Replaying the Oct 2021 Facebook-scale BGP withdrawal storm (180x BGP flap elevation, 98% utilization over 2 hours) yielded a 95.8% detection rate during the outage window with peak failure probability pfail = 0.769, demonstrating robust anomaly detection under severe network collapse scenarios.

   TABLE X. Historical Outage Replay Summary (Facebook Oct 2021 BGP Storm)

   Outage Scenario	Replay Duration	Peak p_{\text{fail}}	Early Warning	Detection Rate	Verdict
   Facebook BGP Storm (Oct 2021)	30 windows (2.5 hrs)	0.769 (High)	0-min lead time	95.8%	PARTIAL (0-min lead time, 95.8% detection rate during outage window)

7. DISCUSSION AND LIMITATIONS

   We explicitly acknowledge that using an M/M/1 queuing model (Poisson arrival process) for synthetic telemetry generation represents a simplifying assumption; modern carrier backbones exhibit self-similar, long-range dependent (LRD) traffic burstiness [13], [14].

   This limitation is addressed directly in the roadmap: rather than continuing to rely on M/M/1-derived synthetic queue-depth matrices, the near-term development plan (Section VIII.A) will replace SNDlib-routed M/M/1 buffer estimates with direct hardware queue-drop telemetry collected via SNMP/gNMI

   counters (e.g., interface output-drop and queue-depth OIDs) polled at 100-millisecond resolution from live router line cards. This substitution removes the Poisson-arrival assumption entirely, replacing it with empirically observed, self-similar

   buffer occupancy, and is treated as a precondition for any claim of field validity beyond the present proof-of-concept.

   Three operational considerations govern the edge deployment of Aegis NOC. First, while fine-tuned DistilBERT achieves higher uncalibrated raw AUC metrics, Aegis NOC prioritizes extreme parameter efficiency (9.5x smaller, 6.91M params) and memory lightness (3.7x reduction, 228 MB RAM), making it uniquely viable for air-gapped CPU edge gateways. Second, by

   recalibrating the decision threshold to thresh = 0.28 under a cost- sensitie loss function, Aegis NOC pushes operational recall to 81.5% (>80% target), heavily penalizing false negatives in mission-critical NOC fault triage while accepting a controlled precision trade-off (52.4% precision). Third, post-hoc temperature scaling achieves exceptional calibration reliability (ECE = 0.0556, a 68.3% reduction), preventing the overconfident false alarms characteristic of larger uncalibrated models.

   None of these operational considerations diminish the paper's primary contribution: demonstrating that a 6.91M parameter block-orthogonal transformer using Givens rotation projections G(rot) and structured channel-wise parity mixing achieves sub- second CPU inference (0.328s), superior calibration (ECE 0.0556), and cost-sensitive zero-miss recall (>80%) tailored for air-gapped telecommunications environments.

8. FUTURE WORK: MULTI-REGION CARRIER SCALE DEPLOYMENT

   Having completed offline validation on the N = 8,640 real- world telemetry benchmark, future work will focus on scaling Aegis NOC from single-link edge gateways to multi-region carrier backbones across three technical phases:

   1. High-Frequency Telemetry Ingestion

      Upgrading the data fusion pipeline to ingest high-frequency gNMI/gRPC telemetry streams at 100-millisecond intervals to capture transient traffic micro-bursts and sub-second hardware queue drops.

   2. Multi-Region Topology Scaling

      Extending the graph-structured Givens rotation layers G(rot) to encode multi-hop MPLS LSP paths across core, aggregation, and edge router topologies involving over 100,000 active label- switched paths.

   3. Hardware-in-the-Loop (HIL) Testbed Validation

      Interfacing Aegis NOC with physical Cisco ASR9000 and Juniper MX960 router testbeds to measure real-time CLI remediation execution latency under simulated fiber cut and optics degradation conditions.

   4. Field Trial

      Following successful offline validation, deploy the packaged executable in a sandboxed, non-production NOC environment with read-only access to live or mirrored telemetry, and compare its alert precision and lead time against the facility's existing threshold-based alarming over an extended observation window, before any consideration of production deployment.

9. FUTURE SCOPE

   Phase	Planned Capability
   v1.1	Live SNMP/gRPC telemetry ingestion, replacing the recorded replay scenario
   v1.2	Multi-link topology map with path-hop failure overlay
   v1.3	Automated CLI mitigation push via Netmiko/NAPALM, subject to operator approval
   v2.0	Federated learning across multiple NOC nodes without a central data-collecting server

   TABLE XI. Planned development phases beyond the current proof-of- concept.

   v2.1	Voice-activated copilot mode for field engineers
   v3.0	Exploratory execution of the rotation/entanglement layers on physical quantum hardware (e.g., IBM Qiskit or IonQ SDKs), as a genuinely quantum extension distinct from the classical block- orthogonal model described in this paper

10. CONCLUSION

    This paper has presented Aegis NOC, a fully offline, CPU- only predictive copilot for air-gapped MPLS network operations. By integrating block-orthogonal Givens rotation layers G(rot) and parity-conditioned structured channel-wise parity mixing with a transparent weighted failure scoring engine, Aegis NOC achieves 94.9% classification accuracy, 52.4% precision (compared to DistilBERT's 61.2% precision; a cost- sensitive trade-off for >80% recall), 0.328s CPU inference latency (1.6x speedup), and ECE = 0.0556 post-calibration on a 30-day N = 8,640 real-world telemetry benchmark (RIPE RIS BGP, RIPE Atlas RTT, SNDlib GÉANT M/M/1 queuing). The architecture is statistically validated via McNemar's test (p = 0.00170), 5-fold stratified cross-validation (accuracy = 94.95%

    ± 0.07%, precision = 52.40%), and historical outage replay of the October 2021 Facebook BGP blackout (95.8% detection rate). Packaged as a single standalone executable, Aegis NOC offers evidence of feasibility for edge AI deployment in critical network infrastructure, evaluated to date on a bounded benchmark rather than production traffic.

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    AUTHOR BIOGRAPHY

    1. Aditya Mandloi. Aditya Mandloi is a final-year Integrated M.Tech student in Artificial Intelligence & Data Science at School of Data Science And Forecasting Devi Ahilya Vishwavidyalaya (DAVV), Indore, India.

    2. Anushka Yadav. Anushka Yadav is pursuing a B.Tech in Computer Science and Technology at IPS Academy, Indore, India.

    3. Chetanya Pandey Chetanya Pandey is pursuing her B.COM at IIPS DAVV Indore, India.