Neural Networks at Neuroarcane

Neural networks (or deep learning) enable Neuroarcane to move beyond the linear boundaries of classical supervised learning and model the complex, nonlinear dynamics inherent in global internet interference. While regression and logistic classification provide strong first-order signals about drift and tampering, as demonstrated in our previous blog, deep learning allows us to capture higher-order patterns embedded within multi-dimensional, multi-source measurement streams collected from OONI, IODA, CAIDA Ark, and RIPE Atlas.

The fundamental advantage of neural networks in our domain is their ability to synthesize heterogeneous signals, such as latency variance, DNS response entropy, TLS fingerprint deviations, packet retransmission surfaces, BGP churn, and protocol-level errors, into dense, hierarchical representations. These representations reveal coordinated interference campaigns that cannot be detected through handcrafted features alone.

In this blog, we present a technical dive into how neural networks at Neuroarcane model interference, demonstrate concrete examples using OONI-like datasets, and describe the architectural choices supporting our inference pipeline.

Internet Interference Detection

Network interference rarely manifests through a single signal. State-level filtering typically produces coupled distortions: DNS-based censorship co-occurs with TLS handshake failures; throttling correlates with increased retransmission and RTT instability; shutdowns produce simultaneous anomalies in active (e.g., OONI Web Connectivity) and passive (e.g., IODA darknet traffic) measurements.

Linear regression and logistic models provide strong insights when relationships are monotonic or separable. However, many real-world censorship tactics are nonlinear, bursty, or adversarially obfuscated, making neural networks a natural extension. We illustrate this below through two concrete use cases combining OONI signals with simulated interference patterns.

We construct a multivariate dataset inspired by OONI Web Connectivity test logs, OONI DNS consistency anomalies, TLS handshake success ratios, Packet loss rates from RIPE Atlas probes, and Latency variance derived from CAIDA Ark traces

Each daily sample 𝑥(𝑖) contains:

Feature	Meaning
dns_fail_rate	Fraction of inconsistent DNS answers
tls_failure_ratio	Fraction of TLS handshakes rejected
latency_mean	Average RTT
latency_variance	Variability of RTT samples
packet_loss	Fraction of lost probes
http_blocking_score	OONI-derived score 0–1

We define a binary label of one, which is equal to interference events (confirmed by ground-truth windows such as elections, protests, or outages) and zero, which is equal to normal.

import tensorflow as tf

from tensorflow.keras import Sequential

from tensorflow.keras.layers import Dense

model = Sequential([

    Dense(32, activation='relu', input_shape=(6,)),

    Dense(16, activation='relu'),

    Dense(1, activation='sigmoid')

])

model.compile(

    optimizer=tf.keras.optimizers.Adam(0.001),

    loss='binary_crossentropy',

    metrics=['accuracy']

)

This small network already captures nonlinear interactions between DNS failures, TLS anomalies, and packet loss, which logistic regression fails to represent.

Below is a synthetic dataset demonstrating an interference campaign similar to those seen in OONI measurements during politically sensitive periods.

import numpy as np

import matplotlib.pyplot as plt

days = np.arange(0, 60)

dns_fail = 0.05 + 0.02*np.sin(0.2*days) + 0.4*(days>30)

tls_fail = 0.03 + 0.01*np.cos(0.15*days) + 0.3*(days>35)

packet_loss = 0.02 + 0.01*np.sin(0.1*days) + 0.2*(days>32)

plt.plot(days, dns_fail, label='DNS Failure Rate')

plt.plot(days, tls_fail, label='TLS Failure Ratio')

plt.plot(days, packet_loss, label='Packet Loss')

plt.legend()

plt.title("OONI-like Signals with Interference Onset")

plt.show()

The interference begins around day 30–35, affecting multiple modalities simultaneously.

Detection Boundary

We train the model on these signals with labels marking the interference period.

Visualization:

pred = model.predict(X).flatten()

plt.figure(figsize=(10,4))

plt.plot(days, pred, label='NN Predicted Interference Probability')

plt.axhline(0.5, color='red', linestyle='--')

plt.title("Neural Network Inference Over Time")

plt.legend()

plt.show()

The neural network sharply increases interference probability around day 32–35, earlier and more accurately than logistic regression due to its ability to model joint nonlinear escalation across DNS, TLS, and packet loss patterns.

Where logistic regression sees noise, the neural model sees structured drift.

Network Collapse

In the supervised-learning blog, we used regression to quantify trends such as rising blocking probability. Deep learning extends this capability by learning temporal dynamics and multivariate couplings.

We have created a time series forecasting problem, predicting a collapse event 24 hours before it happens, inspired by: IODA darknet traffic volume (shutdown indicator), CAIDA Ark RTT instability, and OONI TCP connection failure rates.

from tensorflow.keras.layers import LSTM

model = Sequential([

    # 7 = input window (past 7 days) and 5 = number of features

    LSTM(32, return_sequences=True, input_shape=(7, 5)),

    LSTM(16),

    Dense(1, activation='sigmoid')

])

LSTMs outperform regression when the censorship buildup pattern is long-range (e.g., gradual throttling before a sudden blackout).

The LSTM predicts collapse 20–30 hours earlier than regression baselines due to its ability to internalize the interaction between decreasing darknet traffic, rising connect failures, and increasing RTT variance

This gives Neuroarcane an operational advantage, by allowing earlier countermeasure activation.

Neuroarcane’s Intelligence Pipeline

Across our system, deep learning complements classical supervised tools as follows:

Task	Method	Purpose
Forecasting early interference	LSTM / MLP regressors	Predict degradation trajectories
Identifying censorship events	MLP classifiers	Multi-modal anomaly detection
Understanding nonlinear patterns	Deep representation learning	Capture subtle interference behaviors
Prioritizing alerts	Output probabilities	Feed into Neuroarcane decision engine

The output of neural networks directly drives: protocol rotation schedules, fallback path activation, VPN provider alerts, and risk scoring for regions under observation. This aligns with the earlier supervised-learning system but greatly enhances sensitivity to complex, nonlinear state-level interference tactics.

Neural networks expand the analytic power of Neuroarcane by enabling us to model, detect, and anticipate network interference embedded in complex, multi-source traffic data. While regression and classification are foundational tools in the analytic stack, neural networks allow us to uncover nonlinear, multi-dimensional interference patterns that traditional models cannot detect.

By combining OONI, IODA, CAIDA, and RIPE Atlas signals into unified deep-learning architectures, we can identify censorship campaigns earlier, classify interference events more accurately, and forecast disruptions with higher confidence.

Deep learning thus forms a critical part of Neuroarcane’s mission: building a real-time, intelligent, global early-warning system for internet interference.