AI and Machine Learning for Anomaly Detection: Shadows in the Neural Network

Shadows in the Neural Net: Detecting Anomalous Patterns

Cybersecurity has always relied on its historical foundations, but it also demands a sharp look at the technological present. The neural networks that protect us are, at the same time, mirrors of our own biases and vulnerabilities. Anomaly detection has become a pillar to maintain the integrity of critical systems: spotting data that deviates from the norm is key to anticipating fraud, hardware failures, or intrusions. In this piece, we cross the line between narrative and technique to explain how these “shadows” emerge and what they mean.

When AI Stops Being Neutral

The neutrality of AI is an illusion. Every model we train inherits the distribution of data we provide—and with it, our biases. In practice, an anomaly is any observation that differs significantly from expectations. In a hyperconnected world, those deviations can be early signals of a failing sensor, a targeted attack, or financial fraud. The task of anomaly detection is therefore to recognize the unexpected without falling into false alarms.

Autoencoders and the Machine’s Self-Awareness

Among the most popular methods for finding outliers are autoencoders. These neural nets are trained to reproduce their own input through a bottleneck. The result is an encoder that compresses the signal and a decoder that tries to reconstruct it. The difference between the original and the reconstruction is known as the reconstruction error. If this error surpasses a certain threshold, the data is flagged as anomalous.

Basic autoencoders can be improved in several ways. Denoising Autoencoders and convolutional variants force the network to learn more robust representations and detect anomalies that linear PCA misses. Models like DAGMM add a second network that fits a Gaussian mixture in the latent space, measuring membership probability to derive an anomaly score. Other work uses ensembles such as MSCRED, where multiple ConvLSTM networks reconstruct temporal signatures and combine their outputs to boost accuracy. The lesson is simple: the machine must remember enough of its input to know when something deviates too much.

Patterns and Distances: Measuring Strangeness

Not everything is about reconstruction. Many approaches look directly at a model’s internal representations. A simple strategy is to set a confidence threshold: if the probability assigned by the network to its prediction falls below that minimum, the data is considered out-of-distribution. Another option is measuring distances: Mahalanobis distance calculates how far a point is from the training set mean, and k-nearest neighbour methods compare a point with its closest neighbours; if they belong to another class or are too far away, the point is anomalous.

Generative methods also exist. Variational Autoencoders (VAEs) learn to reconstruct data probabilistically; data with low probability under the model is flagged as anomalous. GANs can model complex distributions and check if an example falls outside what the generator is able to produce. These techniques are powerful but require heavy training and can be fragile.

Ensembles and Bayesian networks, meanwhile, use disagreement between multiple models or uncertainty in weights to flag outliers; if different networks cannot agree, there may be something odd in the input. ODIN, a well-known technique in computer vision, uses temperature scaling and small input perturbations to widen the gap between known and unknown data.

Auditing the Black Box

Having an anomaly score is not enough; one must decide where to cut. The standard practice is to analyze the ROC curve and choose the threshold that best balances true positive rate (sensitivity) and false positive rate (specificity). The area under the curve (AUC) is a useful metric to compare models. In domains with very few anomalies, the F1-score is often more informative than precision alone.

Even before training, auditing the data is crucial. An autoencoder fed with mislabeled records will learn spurious correlations. Recent studies preprocess data with feature scaling, categorical encoding, and selection of relevant attributes. That initial cleaning is as important as choosing the architecture itself.

From Lab to SOC: Obstacles and Attacks

Research labs are controlled environments; security operation centers (SOCs) are not. Here, anomaly detection must work in real time, with noisy data and active attackers. Models are also vulnerable to adversarial attacks: tiny input perturbations that cause the detector to miss malware or flag legitimate data as malicious. To mitigate this, techniques such as Autoencoder Projection and adversarial noise training have been proposed to harden models. Still, in a SOC human oversight is indispensable: not even the most advanced AI replaces the analyst who interprets alerts and adjusts thresholds.

Emerging Trends and Conclusions

The field of anomaly detection is evolving fast. Contrastive learning and Deep SVDD aim to define a sphere in latent space that covers normal data and treats the rest as anomalies. Graph Neural Networks detect anomalous nodes or subgraphs in complex networks; federated learning enables distributed detectors without sharing sensitive data; diffusion models promise to handle even more complex distributions. All of this points to more robust and adaptable detection systems.

In the end, the lesson is cultural: our neural networks are only as neutral as the data and assumptions we give them. Detecting an anomaly is not just preventing an attack, but recognizing our own limitations and biases. In the next piece we may return to a historical episode, but this dive into the guts of AI shows how the past and the future of cybersecurity are linked by the need to understand what happens when the machine reveals its shadows.

Glossary

Anomaly Detection: Process of identifying data points, events, or patterns that deviate from the expected behavior.

Autoencoder (AE): Neural network that learns to compress data and then reconstruct it. If reconstruction fails, the data is likely anomalous.

Reconstruction error: Difference between original data and its autoencoder reconstruction. The larger it is, the more likely the data is anomalous.

Denoising Autoencoder (DAE): Autoencoder trained with noise added to the input so the model learns more robust representations.

Convolutional Autoencoder (CAE): Autoencoder that uses convolutional layers, useful for images or data with spatial/temporal structure.

DAGMM (Deep Autoencoding Gaussian Mixture Model): Model that combines an autoencoder with a Gaussian mixture network in the latent space to generate anomaly scores.

MSCRED (Multi-Scale Convolutional Recurrent Encoder-Decoder): Technique that uses multiple ConvLSTM networks to reconstruct temporal signatures and detect anomalies.

k-NN (k-nearest neighbours): Method that compares a point to its closest neighbors; if they are too far away or belong to another class, the point is anomalous.

Ensemble Methods: Techniques that combine multiple models to improve detection accuracy and reduce errors.

Bayesian Networks: Probabilistic models that use uncertainty in parameters or structure to identify unexpected data.

ROC curve: Graph comparing a model’s ability to distinguish between normal and anomalous data. Used to choose the optimal threshold.

AUC (Area Under the Curve): Global measure of a classifier’s performance based on the ROC curve.

Threshold: Cutoff value beyond which data is classified as anomalous.

F1-score: Metric that balances precision and recall, especially useful when anomalies are rare.

Variational Autoencoder (VAE): Probabilistic version of autoencoders, measuring the likelihood that data fits what the model considers “normal.”

GAN (Generative Adversarial Network): Model that pits two networks (a generator and a discriminator) against each other to create realistic data. If something doesn’t look like what the generator produces, it may be anomalous.

ODIN: Method that applies small perturbations to inputs to see if the model remains confident in its prediction. Useful for detecting unknown data.

Mahalanobis distance: Mathematical formula measuring how far a point is from the “cloud” of normal data.

Adversarial attacks: Tricks designed to fool AI with minimal changes that humans cannot perceive.

Deep SVDD: Method that creates a “sphere” in latent space containing normal data. Anything outside is flagged as anomalous.

Graph Neural Network (GNN): Network type that understands relations between nodes (e.g., devices in a network) and detects when a node behaves oddly.

SOC (Security Operations Center): A facility where security analysts monitor and manage incidents in real time.

Contrastive Learning: Learning approach where the model is trained to distinguish between similar and dissimilar examples to create better representations.

Federated Learning: Approach where multiple devices train models locally and only share parameters instead of raw data, preserving privacy.

Diffusion Models: Generative models that learn to produce data by progressively denoising random noise.

R. Adams