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# GANSec: Enhancing Supervised Wireless Anomaly Detection Robustness through Tailored Conditional GAN Augmentation

Jiali Xu, Shuo Wang, Valeria Loscri, Alessandro Brighente, Mauro Conti, Romain Rouvoy

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## To cite this version:

Jiali Xu, Shuo Wang, Valeria Loscri, Alessandro Brighente, Mauro Conti, et al.. GANSec: Enhancing Supervised Wireless Anomaly Detection Robustness through Tailored Conditional GAN Augmentation. ESORICS 2025 - 30th European Symposium on Research in Computer Security, Sep 2025, Toulouse, France. hal-05137717

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https://hal.science/hal-05137717v1

Submitted on 1 Jul 2025

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# GANSec: Enhancing Supervised Wireless Anomaly Detection Robustness through Tailored Conditional GAN Augmentation

Jiali Xu¹, Shuo Wang², Valeria Loscri¹, Alessandro Brighente², Mauro Conti², and Romain Rouvoy¹,³

¹ Inria centre at the University of Lille, 59650 Villeneuve d'Ascq, France
{firstname}.{surname}@inria.fr

² University of Padova, 35131 Padova, Italy
{firstname}.{surname}@unipd.it

³ University of Lille, CNRS, 59000 Lille, France
{firstname}.{surname}@univ-lille.fr

**Abstract.** Data augmentation techniques show potential in various domains, yet their application to enhance robustness in wireless anomaly detection remains underexplored. Wireless datasets often suffer from anomaly scarcity and class imbalance, hindering the training of reliable detection models. This work introduces GANSec, a novel conditional Generative Adversarial Networks (GAN) framework specifically designed to augment wireless time-series data. We investigate different neural network architectures (MLP, LSTM, CNN) and two conditional training objectives (Embedded Conditional, Classification Oriented) within GANSec, evaluating the framework using real-world 5G measurements for jamming anomaly detection. For evaluation, we train the downstream anomaly detector exclusively on GANSec-generated data and test its performance in a cross-scenario setting. Our evaluation demonstrates that models trained this way significantly outperform those trained on original or baseline augmentation data when tested under unseen network conditions. Specifically, our approach achieved up to 92.13% accuracy on the unseen dataset (i.e., data collected from a different distribution reflecting network conditions distinct from the training set), compared to 78% for models trained on raw data and 83.33% for the best-performing baseline, exhibiting substantially enhanced robustness and generalization.

**Keywords:** Wireless Security · Anomaly Detection · Data Augmentation · Generative Adversarial Networks (GAN) · Cross-Scenario Generalization.

## 1 Introduction

Wireless networks form the backbone of modern communication, supporting applications from personal devices to critical infrastructure, making their security paramount [35, 16]. However, the open nature of wireless communication inherently leaves these networks vulnerable to a diverse array of security threats and
anomalies [1], including sophisticated attacks targeting the physical layer like jamming [32]. Anomaly detection—the identification of significant deviations from normal behavior—is therefore pivotal for safeguarding wireless environments against security breaches, network malfunctions, and other detrimental activities [36, 29, 38]. Accurate and early identification of these irregularities is crucial for maintaining the security, performance, and reliability of wireless services. This need becomes even more critical as reliance on wireless connectivity escalates across sectors like the Internet of Things (IoT), which introduces numerous potential entry points for malicious actors [35, 16, 36]. Robust anomaly detection mechanisms, capable of adapting to the dynamic nature of wireless channels, represent a critical defense against this expanding threat landscape.

However, developing effective anomaly detection models for wireless security faces significant data-related hurdles, including anomaly scarcity, class imbalance, environmental non-stationarity, and the high cost of data acquisition. These challenges, which impede model training and generalization, are discussed in detail in Section 2.

Data augmentation emerges as a potential strategy, with Generative Adversarial Networks (GAN) offering a sophisticated approach compared to simpler techniques like SMOTE [5] and its variants [8]. GAN have been investigated for augmenting network and wireless signal data in security contexts [1, 3, 18, 19, 34, 35]. However, significant limitations persist in prior work, particularly concerning the lack of rigorous validation for robustness and generalization in diverse wireless conditions, and often limited control in generation. These challenges are further elaborated in Section 3.3.

In this work, we propose GANSec to address these challenges: a novel conditional GAN framework specifically designed and tailored for augmenting wireless time-series data. Within GANSec, we investigate different neural network backbones (MLP, LSTM, CNN) and conditional training objectives (Embedded Conditional, Classification-Oriented). To assess GANSec's effectiveness, we perform a challenging cross-scenario evaluation tackling jamming anomalies in 5G networks. In this evaluation (Section 5), we train the downstream anomaly detection model using GANSec-generated data and test its performance on unseen data from different network conditions. Our results demonstrate that models trained using GANSec significantly outperform baseline methods when tested on this unseen data, showcasing enhanced robustness and generalization capabilities.

This work makes the following contributions to robust wireless anomaly detection using generative augmentation: (1) **Novel Conditional GAN Framework (GANSec)**: Designed and implemented GANSec, tailored for augmenting labeled wireless time-series data to generate high-fidelity samples. (2) **Architectural & Objective Impact Analysis**: Systematically analyzed the influence of backbones (MLP, LSTM, CNN) and conditional objectives (EC, CO) within GANSec on data quality and downstream performance. (3) **Demonstrated Robustness & Generalization Enhancement**: Showcased via cross-scenario 5G evaluations that GANSec significantly improves detector robustness against distribution shifts, outperforming baselines.
## 2 Motivation

The pursuit of robust anomaly detection in wireless security faces significant hurdles related to data acquisition and quality, motivating the development of sophisticated data augmentation techniques. We argue that GAN offer a particularly compelling approach to overcome these challenges and enhance detection capabilities, driven by the following key factors:

### 2.1 The Prohibitive Cost of Wireless Data Acquisition

A significant practical barrier hinders the development of robust wireless anomaly detection models: acquiring large-scale, diverse, and representative real-world data. Collecting comprehensive datasets proves particularly challenging, especially for capturing sufficient instances of the rare anomalies crucial for security monitoring, often demanding prohibitive investments in time and resources [20, 27]. Standard measurement intervals dictate that accumulating datasets adequate for training complex machine learning models can require days, weeks, or even months of continuous monitoring per location [28]. This process frequently requires deploying specialized hardware and software, incurring substantial capital and operational costs [7, 20, 27]. Furthermore, operating this measurement equipment continuously over extended periods consumes considerable energy, adding to the overall expense and environmental footprint of large-scale data acquisition campaigns [2]. Capturing high-quality data within dynamic wireless environments, subject to fluctuating channel conditions like multipath fading and interference, adds considerable complexity. This inherent difficulty in obtaining sufficient real-world data, especially for infrequent anomaly events, directly impedes the development and reliable training of robust anomaly detection models. Consequently, cost-effective and scalable alternatives are needed. GAN-based data augmentation offers such an alternative, enabling the generation of large volumes of high-quality synthetic wireless data to supplement limited real-world samples without requiring prolonged and expensive physical data collection campaigns.

### 2.2 Data Quality Limitations and the Naivety of Traditional Augmentation

Beyond scarcity, real-world wireless data frequently suffers from quality limitations [16, 28]. It can be incomplete, noisy, challenging to model accurately, and may fail to represent the full spectrum of potential normal and anomalous behaviors. These quality issues compound the difficulties caused by data scarcity, further complicating the training of reliable detection models.

Data augmentation techniques, often applied in the computer vision domain (e.g., random noise injection, rotation), are generally inadequate for the intricate nature of wireless time-series data [34]. Such naive transformations typically disregard crucial characteristics, such as temporal dependencies between measurements and the underlying statistical properties of wireless signals, potentially
generating unrealistic or misleading samples. Similarly, techniques designed for tabular data, like SMOTE [5] or DeepSMOTE [8], primarily interpolate between existing samples. While helpful for addressing class imbalance, this interpolation approach often fails to capture the complex, non-linear features characteristic of anomalous wireless signals or network traffic, essentially creating averages rather than truly novel and representative examples [18].

In contrast, GAN [9] offer a more sophisticated approach. GAN can learn the complex, high-dimensional probability distributions underlying wireless data. This capability allows them to generate synthetic samples—including plausible representations of rare anomalies—that are statistically similar to real-world data yet potentially more diverse than simple interpolations. By learning the data distribution, GAN can overcome the limitations of simpler augmentation methods and provide more valuable and realistic training signals for anomaly detection models.

## 2.3 Enhancing Model Generalization and Robustness

A primary challenge for wireless anomaly detection systems is ensuring they generalize and remain robust across diverse operational conditions [36]. Super-vised models, while potentially accurate under training conditions, often struggle when deployed in real-world wireless settings due to the environment's inherent dynamism [23]. Factors such as moving obstacles, electromagnetic interference, user mobility, and changing weather continuously perturb the physical commu-nication channel [21].

Consequently, a model trained under one set of environmental conditions (e.g., outdoor conditions, obstacles, etc.) frequently experiences significant per-formance degradation when encountering data reflecting different circumstances [26]. The model's learned decision boundary becomes suboptimal as the underlying data distribution shifts, leading to increased false positives (mistaking environ-mental variations for anomalies) and false negatives (missing true anomalies whose characteristics are altered or were unrepresented in training). This con-stant flux makes defining a stable baseline for "normal" behavior difficult. There-fore, developing anomaly detection methods that exhibit enhanced robustness and generalization is essential for reliable operation. This necessitates exploring techniques, such as advanced data augmentation strategies, capable of equipping models to handle the inevitable data shifts induced by environmental variability.

## 3 Related Work

The challenges outlined in Section 2—data scarcity, quality limitations, and the need for robust anomaly detection in dynamic wireless environments—motivate a review of existing approaches and their limitations. While various anomaly detection techniques exist, the unique characteristics of wireless data necessitate specialized solutions, increasingly pointing towards advanced data augmentation methods like GAN.
## 3.1 Anomaly Detection in Wireless Security

Anomaly detection in wireless security aims to identify deviations from normal operational behavior, which could signify faults or attacks [36]. However, accurately modeling "normal" behavior is difficult due to the dynamic nature of wireless systems, constantly shifting baselines, and unique physical layer vulnerabilities like jamming [29, 38, 26]. Classical methods, including statistical modeling, rule-based systems, and traditional machine learning [29], often struggle with the high dimensionality [31, 30], non-stationarity [33, 12, 25], severe class imbalance [5, 17, 18], and the need for continuous updates or extensive feature engineering common in wireless security datasets [35]. Even deep learning techniques like autoencoders [8, 14], while powerful, traditionally require vast amounts of representative data and can be biased towards abundant normal samples, potentially missing subtle or rare anomalies. These are challenges directly related to the data scarcity and quality issues discussed previously (Section 2.1, 2.2).

## 3.2 Data Augmentation Techniques for Wireless Anomaly Detection

Data augmentation offers a compelling strategy to address the data limitations hindering effective wireless anomaly detection. Among various techniques, GAN [9] have garnered significant attention due to their ability to learn complex data distributions and generate high-fidelity synthetic samples [18, 34, 1]. While other generative models like diffusion models [14, 7] also show promise, we focus on GAN due to their established use in related tasks and architectural flexibility. The fundamental architecture of GAN is detailed in Section 4 where we describe our proposed framework.

Researchers have applied GAN to augment various types of data in wireless security.

**Network Traffic Data:** GAN generate synthetic network flows or packet data, primarily to enrich datasets for Intrusion Detection Systems (IDS) [18]. This helps balance classes by creating more examples of underrepresented attacks and can potentially simulate novel attack variants based on learned patterns.

**Wireless Signal Data:** GAN augment raw wireless signal measurements (e.g., IQ samples, channel state information) for tasks such as physical layer authentication [1] or detecting spectrum anomalies [35, 34], including jamming or unauthorized device transmissions.

Specific examples illustrate the active research in this area. TAnoGAN [3] was proposed for unsupervised time-series anomaly detection using GAN to model normal behavior. Variants like Wasserstein GAN with Gradient Penalty (WGAN-GP) [10, 15] have been employed specifically to combat class imbalance in IDS datasets (e.g., NSL-KDD), improving classifier performance by generating high-quality minority class samples. Other studies [19, 34, 13] report improved
accuracy in cyber-attack detection by training models on GAN-augmented data. These applications highlight GANs' versatility in addressing data scarcity and imbalance challenges (Section 2.1, 2.2).

## 3.3 Challenges and Limitations in GAN-based Augmentation

Despite this progress, applying GAN to enhance the robustness and generalization of wireless anomaly detection systems—critical requirements identified in Section 2.3—reveals significant challenges and limitations in existing practices.

**Insufficient Robustness and Generalization Validation:** A primary concern is the frequent lack of rigorous evaluation regarding the robustness of models trained on GAN-generated data. Many studies do not adequately test performance against unseen data or network conditions significantly different from the GAN's training set [35, 13, 1, 34, 29]. Ensuring models generalize beyond the learned distribution is crucial for deployment in dynamic real-world environments, yet this aspect often remains underexplored.

**Limited Generation Control and Model Sophistication:** Many existing studies employ basic or non-conditional GAN architectures [1, 35, 34]. These often lack fine-grained control over the generation process (e.g., conditioning on specific labels, environmental factors, or attack parameters). This limits the ability to generate targeted, context-aware synthetic data, which is crucial for training models to handle complex, condition-dependent anomalies.

**Oversimplified Evaluation Settings:** Some evaluations rely on datasets with limited features or utilize simplified simulated wireless environments [1, 18]. Such settings may not capture the full complexity of real-world deployments, potentially overstating performance and limiting the practical applicability of the findings.

These identified gaps—specifically concerning the validation of robustness and generalization in dynamic scenarios—underscore the necessity for more advanced, tailored generative frameworks. Existing approaches often fall short of producing the diverse, high-fidelity, and condition-specific synthetic data required to train wireless anomaly detectors that are truly robust to unseen conditions. This directly motivates our proposed framework, GANSec, detailed in Section 4. GANSec is specifically designed to address these limitations by incorporating conditional generation tailored for wireless time-series data, investigating architectures adept at capturing temporal dependencies, and rigorously evaluating performance with a focus on cross-scenario generalization.

# 4 The GANSec Framework

To address the challenges of data scarcity, quality, and the need for robustness in wireless anomaly detection highlighted previously, we propose GANSec: a
tailored GAN framework designed specifically for augmenting labeled wireless
data. GANSec aims to generate high-fidelity synthetic time-series data that cap-
tures the complex characteristics of wireless signals, including anomalous events,
thereby improving the training and generalization capabilities of downstream
anomaly detection models.

Figure 1 illustrates the overall workflow of our proposed approach. The pro-
cess begins with real-world data acquisition, potentially sourced from distinct
phases or environments (Experiment and Production). Raw signal measurements
captured within these domains, along with relevant contextual information *c*,
first undergo standard preprocessing steps such as data labeling, cleaning, and
feature extraction to yield features *x*. The subsequent temporal aggregation step
structures these extracted features over time into 2D time-series tensors.

In the baseline conventional approach, the processed 2D tensors from the
experiment domain would be used directly to train the anomaly detector. In
contrast, we use these time-series to train the conditional GAN for data aug-
mentation. GANSec adapts the standard GAN paradigm [9], comprising a Gen-
erator (G) and a Discriminator (D), implemented as neural networks. G learns
to transform random noise vectors *z* into synthetic data samples that resemble
real wireless signals, while D learns to distinguish these synthetic samples from
real-world data. Through adversarial training, both networks improve, enabling
G to produce increasingly realistic synthetic wireless data.

<figure><img src="image_3.png" /><figcaption>Fig. 1: GAN-based Data Augmentation for Anomaly Detection in Wireless Security: Workflow Overview</figcaption></figure>

## 4.1 GANSec Architecture and Training Mechanisms

Figure 2 provides a detailed view of the GANSec architecture. G takes a ran-
dom noise vector z, sampled from a standard normal distribution (N(0, I)) with
dimensions defined by the desired latent space size, concatenated with condition-
ing information—specifically the target label y and potentially other contextual
conditions c—as input. These inputs are processed through embedding layers
and a neural network backbone (which we explore using MLP, LSTM, or CNN
architectures) to produce synthetic features x̂. D receives either a real data sam-
ple x or a synthetic sample x̂, along with the corresponding conditions {y, c},
and outputs a likelihood on authenticity; this score challenges $\mathcal{G}$ to produce increasingly realistic data.

<figure><img src="image_4.png" /><figcaption>Fig. 2: Architecture of the GANSec framework. The diagram shows distinct paths and objectives for the Embedded Conditional (EC-Discriminator) and Classification Oriented (CO-Discriminator) training mechanisms described in Section 4.1.</figcaption></figure>

Within this conditional framework, we designed and investigated two primary $\mathcal{D}$ mechanisms for training $\mathcal{G}$, each utilizing different objective functions.

**Embedded Conditional (EC) Discriminator** In this configuration, the primary objective is generating realistic data samples that accurately match the provided conditions $\{y, c\}$. As shown by the EC-Discriminator path in Figure 2, the conditioning information is embedded and fed into $\mathcal{D}$. $\mathcal{D}$ acts solely as a real/fake classifier for the given condition, outputting a probability score indicating authenticity. $\mathcal{G}$, in turn, aims to produce data $\hat{x}$ that the $\mathcal{D}$ classifies as real for the specified condition $\{y, c\}$. This approach emphasizes fidelity to the conditional data distribution. The training process follows Algorithm 1 detailed in Appendix A.

**Classification Oriented (CO) Discriminator** This mechanism extends the $\mathcal{D}$'s role beyond simple real/fake classification, as depicted by the CO-Discriminator path in Figure 2. Here, $\mathcal{D}$ simultaneously performs two tasks: it predicts whether
the input data ($x$ or $\hat{x}$) is real or synthetic, and it predicts the data's class label $y$ based on condition $c$. Correspondingly, $\mathcal{G}$'s loss function encourages the generation of synthetic data $\hat{x}$ that is not only classified as real by $\mathcal{D}$ but is also classified with the intended target label $y$. The intuition is that by optimizing for class discriminability alongside realism, $\mathcal{G}$ learns to generate samples particularly useful for the downstream classification task, potentially enhancing the anomaly detector's performance. The training follows Algorithm 2 detailed in Appendix A.

Comparing these two mechanisms allows us to evaluate distinct strategies for generating effective synthetic data: one focused primarily on conditional realism (EC-GAN) and the other directly incorporating the downstream task objective (CO-GAN). While the CO-GAN might offer advantages for improving classification accuracy by optimizing for class discriminability, it could also be more sensitive to class imbalance in the original training data.

## 4.2 Tailoring GANSec for Wireless Data

Generating realistic synthetic wireless data requires specific adaptations beyond standard GAN implementations, as naive approaches often fail to capture the unique characteristics of wireless signals (as discussed in Section 2.2). GANSec incorporates several design considerations to handle these intricacies effectively.

**Handling Temporal Dependencies:** Wireless signal measurements are time-series data with strong temporal correlations crucial for context [33, 3]. To capture these, GANSec employs backbones suited for sequential data. LSTMs are used for their ability to model long-range dependencies, while 2D CNNs effectively learn local, time-invariant patterns within the signal sequences. An MLP baseline is included for comparison, requiring flattening of the time-series segments into 1D vectors, which potentially obscures some temporal relationships.

**Preserving Statistical Properties:** Beyond temporal structure, wireless signals possess specific statistical properties vital for accurate representation. For instance, metrics like Reference Signal Received Power (RSRP) are often measured on a logarithmic scale (dBm). This logarithmic nature, while convenient for representing a wide dynamic range, introduces challenges for neural networks [6]. The non-linearity can interact suboptimally with standard activation functions if not handled carefully, and common loss functions like Mean Squared Error behave differently in the logarithmic domain compared to the linear power domain [24, 37]. Furthermore, noise and interference cause disproportionately larger changes in dBm values for weaker signals compared to stronger ones [22]. Therefore, generating synthetic data that accurately reflects these statistical properties, especially for log-scale metrics, is non-trivial. While GANSec does not explicitly apply the specialized preprocessing techniques discussed for discriminative Received Signal Strength Indicator (RSSI) models, its generator must
learn transformations that account for these statistical intricacies, and the dis-
criminator's evaluation implicitly pushes the generated data towards realistic
distributions, effectively addressing the challenges posed by logarithmic scales
within the generative framework.

**Incorporating Domain Knowledge:** The conditional nature of GANSec allows us to explicitly guide the generation process using domain-specific information. The condition vector *c* can include parameters representing known environmental factors, device types, protocol states, specific interference signatures, or indicators of known vulnerabilities, enabling the generation of contextually relevant data.

4.3 Synthetic Data Generation and Usage Strategy

Once GANSec is trained on the available real data using either the EC or CO
mechanism, we utilize the trained $\mathcal{G}$ to produce synthetic data. To generate
synthetic samples corresponding to a specific class label and condition $\{y, c\}$,
we sample a random noise vector $z$ and provide the tuple $\{z, y, c\}$ as input to
the trained $\mathcal{G}$. This process, detailed in Algorithm 3 (Appendix A), allows us
to generate any desired number of synthetic samples for each class, potentially
including optional post-processing or validation steps (e.g., using the $\mathcal{D}$ to filter
generated samples based on a quality threshold $t$).

A key aspect of our proposed methodology involves training the downstream
anomaly detection model exclusively on the synthetic data generated by GANSec.
Employing a synthetic-only training strategy offers several significant advantages
compared to using the original real-world data: (1) it allows for the generation of
perfectly balanced datasets, mitigating issues caused by original class imbalance;
(2) if the GAN captures the underlying distribution well, the synthetic data can
exhibit greater diversity than the initial samples, potentially enhancing detector
robustness and generalization; and (3) it offers potential privacy preservation
benefits, as training the anomaly detector solely on synthetic data avoids direct
exposure of potentially sensitive real-world measurements during this critical
phase, which is particularly relevant when dealing with user data or proprietary
network information.

5 Evaluation

This section details the experimental evaluation conducted to validate the effec-
tiveness of our proposed GANSec framework. Following the methodology out-
lined in Section 4, our evaluation rigorously assesses GANSec from two crit-
ical perspectives. First, we quantitatively measure the fidelity and statistical
similarity of the synthetic wireless data generated by different GANSec con-
figurations compared to real-world data. Second, and crucially for addressing
the challenges motivated in Section 2.3, we evaluate the effectiveness of using
GANSec-generated data for training robust anomaly detection models. Particu-
lar focus is placed on the model’s ability to generalize to unseen data collected
under different network conditions, assessed through a challenging cross-scenario
evaluation.

**5.1 Experimental Setup**

**Data Description:** The data used in our experiments consists of real-world 5G wireless measurements collected within the indoor environment illustrated in Figure 3a. The hardware setup, shown in Figure 3b, included a OnePlus Nord 2T 5G smartphone acting as the user equipment (UE), two Nuand bladeRF 2.0 micro SDRs configured as a 5G gNodeB and a jammer to introduce anomalies.

The jammer operated in the 5G n2 uplink frequency band (1850-1910MHz) and was controlled using a sinusoidal power modulation pattern to create disrup- tive signals. This type of power-modulated jamming, with its continuously time- varying pattern, can be particularly challenging to detect compared to simplistic constant or reactive jammers [32]. Preliminary tests confirmed its effectiveness, as the jamming activity was observed to significantly impact the network latency of the UE during iperf3 testing.

We collected radio buffer messages from the Android smartphone via the An-
droid Debug Bridge (ADB), capturing key signal quality indicators. To ensure
data consistency, we parsed raw logs, handled missing values using interpolation,
and normalized the features into uniform time-series segments. Ground truth la-
bels y were assigned based on when the jammer was active, and a condition
vector c was constructed for each sample, including the UE's distance to the gN-
odeB, device model, and modem temperature. This process yielded two datasets
from the distinct setups shown in Figure 3a: Scenario A, used for training, com-
prises 3000 samples collected over approximately 10 minutes and is relatively
balanced, with jamming anomalies representing 46.37% of the data. To evaluate
generalization, Scenario B serves exclusively as the unseen test set and contains
4559 samples collected from a different UE position with an additional obstacle.
This scenario features a different class distribution, with anomalies constituting
65.08% of the samples.

**Baselines for Comparison** We compare the performance achieved using GANSec-augmented data against models trained using three baseline data strategies:

- Raw Data Training: The downstream anomaly detection model trained di-
  rectly on the original Scenario A dataset.

- Classical Oversampling: The downstream model trained on the Scenario A dataset augmented using DeepSMOTE [8], an advanced implementation that notably uses a Variational Autoencoder (VAE) to combine SMOTE [5] principles with deep learning representations.

- Unconditional GAN: The downstream model trained on data generated by TAnoGAN [3], representing another GAN-based approach for time-series anomaly detection, used here as a generative baseline.
<figure><img src="image_5.png" /><figcaption>Fig. 3: Experimental Setup for Collecting Dataset A and B.</figcaption></figure>

**Downstream Anomaly Detection Model** To evaluate the utility of the generated data, we employed a downstream anomaly detection model tasked with classifying wireless measurements as 'normal' or 'anomalous' (jamming). We utilized a 3-layer Long Short-Term Memory (LSTM) network for this purpose, chosen for its ability to capture temporal dependencies in the signal data. Each LSTM layer contained 128 hidden units, followed by a dropout layer with a rate of 0.3 to prevent overfitting, and a final dense layer with a sigmoid activation function for classification. The model was trained using the Adam optimizer with a learning rate of 0.0001 for 128 epochs, utilizing binary cross-entropy as the loss function.

## 5.2 Synthetic Data Quality Analysis

We analyze the quality of synthetic wireless data generated by GANSec variants and baseline methods using Fréchet Inception Distance (FID) [11] and Maximum Mean Discrepancy (MMD) [4] with an RBF kernel. These metrics are commonly used as they can reliably analyze feature distribution similarity. Lower scores indicate higher similarity to the real Scenario A data distribution. To assess the distribution and stability of these metrics, we performed 100 independent generation iterations for each model configuration, creating a synthetic dataset equivalent in size to Dataset A in each iteration. Figure 4 presents the Cumulative Distribution Functions (CDFs) of these scores.

For this process, we applied the optional validation step detailed in Algorithm 3, using the trained discriminator to filter generated samples with a default quality threshold of $t = 0.5$.

The DeepSMOTE [8] oversampling method yields very low FID and MMD scores (e.g., average FID 1.603, MMD 0.164), indicating generated samples closely
<figure><img src="image_6.png" /><figcaption>Fig. 4: Distribution Analysis on Synthetic Dataset</figcaption></figure>

mirror the original data but offer limited diversity due to its interpolative nature (as argued in Section 2.2). In contrast, the TAnoGAN [3] baseline shows low FID (14.762) but high MMD (0.632), suggesting it captures global data structure but struggles with local sample quality or diversity.

Generally, our proposed GANSec variants exhibit higher FID and MMD scores than DeepSMOTE. We interpret these higher scores not merely as lower fidelity, but rather as reflecting GANSec's ability to generate a more diverse set of synthetic samples, while still capturing key signal patterns. This increased diversity is hypothesized to be crucial for improving model robustness and generalization, which we test in Section 5.3. In generative modeling, FID scores for GAN can vary widely depending on the complexity of the data and the model; values in the range observed (e.g., up to 60-70 for CNN backbones) can be considered reasonable [11].

Within GANSec, the choice of neural network architecture influences synthetic data characteristics. MLP-based GAN tend towards lower MMD scores, while LSTM-based GAN show stable behavior across metrics. CNN-based GAN appear more "aggressive," producing potentially more diverse outputs (reflected in higher scores), which might explain the better cross-scenario performance observed later. This suggests a potential trade-off: architectures like CNN might sacrifice exact distributional match (higher FID/MMD) to achieve greater diversity beneficial for downstream task robustness.

## 5.3 Anomaly Detection Performance: Cross-Scenario Evaluation

Having analyzed the synthetic data quality, we now evaluate our primary hypothesis regarding GANSec's ability to enhance anomaly detector robustness and
generalization. We conducted a cross-scenario evaluation, training the LSTM
anomaly detector (detailed in Sec 5.1) on data derived from Scenario A and
testing on unseen Scenario B data. Results are averaged over 10 independent
iterations to mitigate randomness. Notably, all models performed well (>98%
accuracy) on Scenario A data itself, suggesting the synthetic-only training does
not cause catastrophic forgetting. Figure 5 illustrates key accuracy results. The
detailed peak performance metrics for each model are provided in Appendix B.

<figure><img src="image_7.png" /><figcaption>Fig. 5: Impact of the augmentation ratio (relative size of synthetic data used for training) on the accuracy achieved on the unseen Dataset B.</figcaption></figure>

The generalization challenge is highlighted by the baseline LSTM model
trained on raw Scenario A data: its accuracy dropped from approx. 92% when
tested on Scenario A to 78% on Scenario B. This performance degradation un-
derscores both the detection difficulty posed by the dynamic, power-modulated
jammer implemented in our experiments and the substantial generalization gap
faced by standard anomaly detection methods when encountering different net-
work conditions.

In contrast, the results clearly demonstrate the advantage of the GANSec
framework. Models trained exclusively on GANSec-generated data significantly
outperformed all baseline methods on the unseen Scenario B, showcasing en-
hanced generalization by learning more fundamental anomaly features. Consis-
tent with data quality analysis, LSTM and CNN backbones generally outper-
formed MLPs, reinforcing the importance of architectures that effectively model
temporal signal characteristics. Comparing training objectives, the Classification-
Oriented (CO-GAN) objective showed a slight edge over the Embedded Con-
ditional (EC-GAN) variant, suggesting that directly incorporating the classifi-
cation objective during generation may produce more discriminative synthetic
samples.

The superior cross-scenario performance validates our synthetic-only GAN
strategy for building robust detectors. This tailored approach handles unseen
conditions, addressing a key limitation of prior work.
# 6 Discussion

This work demonstrates that GANSec, a tailored conditional GAN framework, significantly enhances the robustness and generalization of wireless anomaly detectors, particularly in challenging cross-scenario evaluations. Our results advocate for moving beyond generic augmentation towards methods designed for wireless data intricacies, where sequence-aware architectures (LSTMs, CNNs) offer advantages. The viability of augmented training also presents practical benefits regarding data imbalance, collection costs, and potential privacy enhancement.

Despite these promising results, we acknowledge limitations. Our current evaluation focused on jamming anomalies within a specific 5G context. However, it is important to note that the framework demonstrated effectiveness against a sophisticated, power-modulated jammer exhibiting dynamic time-varying patterns (as described in Section 5.1), suggesting its potential applicability extends beyond this specific scenario to other challenging wireless anomalies where capturing complex temporal dynamics is crucial. Nevertheless, further investigation is required to assess GANSec's applicability to other anomaly types and wireless technologies (e.g., Wi-Fi, IoT protocols). Additionally, while GAN can generate diverse data, risks remain regarding the potential amplification of biases present in the initial real dataset or the failure to generate truly novel "black swan" anomalies. The computational expense of training advanced GAN models is also a practical consideration.

Future research should prioritize extending GANSec to a wider spectrum of anomalies and network contexts, potentially exploring hybrid approaches combining real and synthetic data. Developing techniques to explicitly encourage the generation of rare or edge-case anomalies and further refining generative architectures (e.g., using attention mechanisms or investigating alternatives like diffusion models, despite their current computational complexity concerns) are promising directions. While diffusion models show potential, their iterative noise-and-denoise sampling process often requires hundreds to thousands of steps to produce a single high-quality sample, making them less suitable for the rapid, large-scale data-augmentation scenarios explored in this work. Crucially, validating enhanced approaches in real-time, operational settings is essential. Finally, while generative models can reduce the environmental impact associated with extensive physical data collection, the energy cost of training these complex models warrants consideration and comparison against traditional methods in future analyses.

# 7 Conclusion

Addressing the need for robust wireless anomaly detection despite data limitations, this work introduced GANSec, a tailored conditional GAN framework. We evaluated a novel strategy using exclusively GANSec-generated synthetic data for training the downstream detector. Cross-scenario evaluations confirmed this
approach significantly enhances model generalization and robustness, effectively addressing the substantial performance degradation observed when anomaly classifiers, trained on limited data, encounter unseen network conditions. Our findings indicate that tailored generative models employing synthetic-only training offer a highly promising and effective path towards developing more reliable wireless security systems.

**Acknowledgments.** This research was made possible with support from the Horizon Europe research and innovation program of the European Union under grant agreement number 101092912 (project MLSysOps) and COST Action CA 22104 BEiNG-WISE.

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# A Algorithms

This appendix details the core algorithms used within the GANSec framework.

**Algorithm 1** Embedded Conditional GAN Training: $\mathcal{D}$ focuses on distinguishing real samples from synthetic samples based on the provided context

**Require:** Feature set $x \sim dist_{\{y,c\}}(x)$ with label $y$ and condition $c$

**Ensure:** Discriminator $\mathcal{D}$, Generator $\mathcal{G}$

1: Initialize number of epochs $\epsilon$, loss function $\mathcal{L}$, loss weights $\omega_i$

2: **for** current_epoch $\leftarrow 1, 2, ..\epsilon$ **do**

3: Sample noise $z \sim \mathcal{N}(0, I)$

4: Generate synthetic data using $\mathcal{G}: \hat{x} \leftarrow \mathcal{G}(z, \{y, c\})$

5: Discriminate real or synthetic data using $\mathcal{D}$

$$output\_real \leftarrow \mathcal{D}(x, \{y, c\})$$

$$output\_synthetic \leftarrow \mathcal{D}(\hat{x}, \{y, c\})$$

6: Compute loss for $\mathcal{G}$ and $\mathcal{D}$

$$loss_{\mathcal{D}} \leftarrow \omega_0 \times \mathcal{L}(1, output\_real) + \omega_1 \times \mathcal{L}(0, output\_synthetic)$$

$$loss_{\mathcal{G}} \leftarrow \omega_2 \times \mathcal{L}(1, output\_synthetic)$$

7: **end for**

**Algorithm 2** Classification Oriented GAN Training: $\mathcal{D}$ performs a dual role: it assesses the realism of the input sample and predicts its class label.

**Require:** Feature set $x \sim dist_{\{y,c\}}(x)$ with label $y$ and condition $c$

**Ensure:** Discriminator $\mathcal{D}$, Generator $\mathcal{G}$

1: Initialize number of epochs $\epsilon$, loss functions $\mathcal{L}_i$, loss weights $\omega_i$

2: **for** current_epoch $\leftarrow 1, 2, ..\epsilon$ **do**

3: Sample noise $z \sim \mathcal{N}(0, I)$

4: Generate synthetic data using $\mathcal{G}: \hat{x} \leftarrow \mathcal{G}(z, \{y, c\})$

5: Discriminate real or synthetic data and predict label using $\mathcal{D}$

$$\{output\_real, y'\} \leftarrow \mathcal{D}(x, c)$$

$$\{output\_synthetic, \hat{y}\} \leftarrow \mathcal{D}(\hat{x}, c)$$

6: Compute loss for $\mathcal{G}$ and $\mathcal{D}$

$$loss_{\mathcal{D}} \leftarrow \omega_0 \times \mathcal{L}_0(1, output\_real) + \omega_1 \times \mathcal{L}_1(y, y') +$$

$$\omega_2 \times \mathcal{L}_0(0, output\_synthetic) + \omega_3 \times \mathcal{L}_1(y, \hat{y})$$

$$loss_{\mathcal{G}} \leftarrow \omega_4 \times \mathcal{L}_0(1, output\_synthetic) + \omega_5 \times \mathcal{L}_1(y, \hat{y})$$

7: **end for**
**Algorithm 3** Synthetic Data Generation: $\mathcal{G}$ produces regulated synthetic feature sets given a target label $\hat{y}$ and condition $\hat{c}$, including an optional step for validating the quality of generated samples using the $\mathcal{D}$ with a threshold $t$

**Require:** Generator $\mathcal{G}$, Discriminator $\mathcal{D}$, label $\hat{y}$ and condition $\hat{c}$

**Ensure:** Synthetic feature set $\hat{x}$

1: Initialize regulations $regul(x)$, discriminator threshold $t$

2: **while** $p < t$ **do**

3: Sample noise $z \sim \mathcal{N}(0, I)$

4: Generate synthetic data using $\mathcal{G}$: $\hat{x} \leftarrow \mathcal{G}(z, \{\hat{y}, \hat{c}\})$

5: Regulate synthetic data: $\hat{x}' \leftarrow regul(\hat{x})$

6: Validate using $\mathcal{D}$: $p \leftarrow \mathcal{D}(\hat{x}')$

7: **end while**

## B Detailed Performance Metrics

To supplement the accuracy results presented in Section 5.3, Table 1 provides a more detailed breakdown of model performance on the unseen Scenario B dataset. For each model, the table reports the peak accuracy, precision, recall, F1, and ROC-AUC score achieved at its optimal data augmentation ratio.

<table><caption>Table 1: Peak Performance Metrics on Unseen Scenario B. For each model, the table reports key metrics at the augmentation ratio that yielded its optimal accuracy.</caption><thead><tr><th>Model</th><th>Aug. Ratio</th><th>Acc.</th><th>Prec.</th><th>Rec.</th><th>F1</th><th>ROC.</th></tr></thead><tbody><tr><td>Raw Data (Baseline)</td><td>N/A</td><td>0.781</td><td>0.914</td><td>0.735</td><td>0.815</td><td>0.802</td></tr><tr><td>DeepSMOTE</td><td>+200%</td><td>0.822</td><td>0.765</td><td>0.794</td><td>0.779</td><td>0.844</td></tr><tr><td>TAnoGAN</td><td>+500%</td><td>0.833</td><td>0.928</td><td>0.806</td><td>0.863</td><td>0.845</td></tr><tr><td>EC-GAN-MLP</td><td>+30%</td><td>0.851</td><td>0.836</td><td><strong>0.981</strong></td><td>0.903</td><td>0.792</td></tr><tr><td>EC-GAN-LSTM</td><td>+150%</td><td>0.910</td><td><strong>0.962</strong></td><td>0.922</td><td><strong>0.942</strong></td><td>0.905</td></tr><tr><td>EC-GAN-CNN</td><td>+150%</td><td>0.883</td><td>0.925</td><td>0.894</td><td>0.910</td><td>0.878</td></tr><tr><td>CO-GAN-MLP</td><td>+20%</td><td>0.836</td><td>0.823</td><td>0.975</td><td>0.893</td><td>0.772</td></tr><tr><td>CO-GAN-LSTM</td><td>+300%</td><td>0.894</td><td>0.936</td><td>0.884</td><td>0.910</td><td><strong>0.922</strong></td></tr><tr><td>CO-GAN-CNN</td><td>+70%</td><td><strong>0.921</strong></td><td>0.945</td><td>0.934</td><td>0.940</td><td>0.915</td></tr></tbody></table>