Closing the Cyber Data Gap with AI-Generated Threats

by

Author: Denis Avetisyan

A new framework utilizes generative models to create realistic synthetic attack data, bolstering defenses against increasingly sophisticated cyber intrusions.

The PHANTOM algorithm leverages a specific network architecture, detailed in Figure 1, to facilitate its operational logic.
The PHANTOM algorithm leverages a specific network architecture, detailed in Figure 1, to facilitate its operational logic.

This paper introduces PHANTOM, a progressive high-fidelity adversarial network combining GANs and VAEs to address data scarcity and class imbalance in cybersecurity datasets.

The limited availability of real-world cyberattack data presents a significant obstacle to developing robust intrusion detection systems. This paper introduces PHANTOM: Progressive High-fidelity Adversarial Network for Threat Object Modeling, a novel framework employing generative adversarial networks and variational autoencoders to synthesize high-fidelity attack data. Evaluation demonstrates that models trained on PHANTOM-generated data achieve near-perfect accuracy in detecting real attacks while preserving authentic data distributions. Could this approach unlock new avenues for creating privacy-preserving, data-augmented cybersecurity defenses?

The Scarcity of Insight: Data Limitations in Cybersecurity

The foundation of robust cybersecurity rests upon the availability of extensive and varied datasets detailing actual attack patterns; however, acquiring sufficient real-world cyberattack data remains a significant and ongoing obstacle. Unlike many areas of machine learning where large, publicly available datasets are commonplace, the cybersecurity landscape is characterized by data scarcity and fragmentation. Organizations are often hesitant to share information about successful breaches due to reputational risks and legal concerns, creating a collective action problem. Furthermore, the very nature of attacks – often designed to be stealthy and avoid detection – limits the amount of recorded data available for analysis. This lack of comprehensive data hinders the development of effective detection systems and predictive models, leaving systems vulnerable to both known and, critically, previously unseen threats. The challenge isn’t simply a matter of quantity, but also of representativeness; existing datasets often over-represent common attacks while under-representing more sophisticated or targeted intrusions, skewing the training of security algorithms.

A substantial challenge in developing effective cybersecurity systems lies in the uneven distribution of data regarding different attack types; this phenomenon, known as class imbalance, significantly complicates model training. While common attacks like Distributed Denial of Service (DDoS) generate abundant data for analysis, rarer but potentially devastating attacks, such as User-to-Root (U2R) exploits, produce comparatively few examples. This disparity leads machine learning algorithms to become heavily biased towards recognizing prevalent attack patterns while struggling to identify and mitigate the more insidious, less frequent threats. Consequently, models may exhibit high accuracy on common attacks but prove largely ineffective against novel or uncommon exploits, leaving systems vulnerable despite appearing secure based on overall performance metrics. Addressing this imbalance requires innovative techniques, such as data augmentation, anomaly detection, and cost-sensitive learning, to ensure robust defenses against the full spectrum of cyber threats.

Conventional cybersecurity systems often falter when confronted with previously unseen or rarely occurring attacks due to their reliance on pattern recognition derived from extensive datasets. These systems are typically trained to identify and respond to frequently observed threats, like Distributed Denial of Service (DDoS) attacks, but struggle to generalize to the long tail of less common exploits. This limitation arises because machine learning models, the backbone of many modern defenses, require substantial examples to accurately categorize and counter threats; a scarcity of data regarding novel or infrequent attacks results in poor performance and increased vulnerability. Consequently, attackers can exploit this weakness by crafting unique attacks that bypass existing defenses, highlighting the critical need for adaptive security measures capable of handling data limitations and identifying anomalous behavior beyond established threat signatures.

PHANTOM: A Synthetic Foundation for Resilience

PHANTOM is a synthetic data generation framework developed to address limitations in available cyberattack datasets, specifically the issues of data scarcity and class imbalance. Existing cybersecurity datasets often lack sufficient examples of diverse attack types, hindering the development and evaluation of robust intrusion detection systems. PHANTOM aims to augment these datasets by producing realistic, artificial attack samples that statistically resemble genuine attacks. This approach allows for the expansion of training datasets without the costs and logistical challenges associated with collecting real-world attack data, and helps to balance the representation of different attack classes, thereby improving the performance of machine learning models designed to detect malicious activity.

PHANTOM utilizes a multi-task learning approach, simultaneously training both Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to synthesize cyberattack data. The VAE component learns a compressed, latent representation of the input attack data, enabling the generation of novel samples through random sampling from this latent space. Concurrently, the GAN component, consisting of a generator and discriminator, refines the generated samples to increase their fidelity and realism. By training these two models in parallel with a shared loss function, PHANTOM leverages the strengths of both approaches: the VAE provides efficient data generation and exploration of the data distribution, while the GAN ensures the generated samples closely resemble real attack instances, resulting in high-fidelity synthetic data.

The PHANTOM architecture utilizes Wasserstein GANs (WGANs) to address common training instabilities associated with traditional Generative Adversarial Networks. WGANs employ a critic, rather than a discriminator, and utilize the Earth Mover’s distance – also known as the Wasserstein-1 distance – as a loss function, providing a more stable gradient during training. Furthermore, PHANTOM incorporates a Feature Matching Loss, which minimizes the distance between the features of generated samples and real samples extracted from a pre-trained feature extractor. This loss function encourages the generator to produce synthetic data that closely resembles real data in terms of its learned feature representations, thereby preserving critical data characteristics and improving the quality of generated cyberattack samples.

Evaluation of the PHANTOM framework on a real-world test set demonstrated a 98% weighted accuracy in detecting common cyberattack types. This performance was achieved by augmenting existing training data with synthetically generated attack samples, effectively addressing the challenges posed by data scarcity and class imbalance. The synthetic data increased the size and diversity of the training set, leading to improved generalization and a statistically significant enhancement in detection capabilities compared to models trained solely on limited real-world data. The weighted accuracy metric accounts for varying class representation, providing a comprehensive assessment of the framework’s performance across all attack types.

The Temporal Logic of Attack: Modeling Sequential Behavior

Attack sequences are not random collections of events; rather, malicious activity progresses through defined stages exhibiting temporal dependencies. These dependencies represent the order and timing relationships between individual actions within an attack. For example, a reconnaissance phase typically precedes exploitation, and data exfiltration follows successful access. Accurate modeling of these dependencies is vital for security systems as it allows for the prediction of future actions based on observed events. Detection systems that account for temporal order significantly reduce false positives and improve the identification of multi-stage attacks, increasing the efficacy of incident response and mitigation efforts.

PHANTOM generates synthetic attack data by explicitly modeling temporal relationships between actions within an attack sequence. This is achieved through the definition of state transitions and dependencies, where the occurrence of one action increases the probability of subsequent actions. The framework doesn’t rely on random sequencing; instead, it constructs attacks based on predefined or learned patterns of malicious behavior. By simulating the order and timing of events, PHANTOM creates realistic attack scenarios that accurately represent the progression of real-world threats, enabling more effective training and evaluation of security systems designed to detect such sequences.

Progressive Training within PHANTOM involves a staged approach to synthetic data generation, beginning with the modeling of broad, high-level attack phases. Initial training iterations focus on capturing the general sequence of events without detailed temporal precision. Subsequent iterations progressively refine the model by incorporating increasingly granular features, such as inter-event timings and specific parameter variations. This incremental refinement allows the framework to learn and reproduce complex temporal patterns inherent in real-world attacks, effectively building a representation of attack sequences from coarse to fine detail. The method addresses the challenge of modeling intricate temporal dependencies by avoiding the need to simultaneously learn all levels of detail, leading to more robust and realistic synthetic data.

The fidelity of synthetic data to real-world attack timelines directly impacts the efficacy of trained security models. By accurately replicating the temporal characteristics-duration, ordering, and intervals-of observed attacks, PHANTOM enables the creation of datasets that better represent the nuanced behavior of threat actors. This improved realism facilitates more robust model training, leading to enhanced detection rates and reduced false positives when applied to live network traffic. Specifically, models trained on temporally accurate synthetic data demonstrate a greater capacity to identify attacks that unfold over time, as opposed to static, isolated events, thereby improving overall security posture.

Beyond Reaction: Proactive Cybersecurity with Synthetic Data

Security models benefit significantly from the incorporation of synthetically generated data, as demonstrated by the PHANTOM system. This data augmentation technique effectively expands training datasets, leading to demonstrably improved detection rates of malicious activity and a concurrent reduction in false positive alerts. By exposing models to a wider range of scenarios, including those infrequently observed in real-world data, the system enhances their ability to generalize and accurately identify threats. The resulting models are not only more sensitive to actual attacks but also less prone to misclassifying benign activity, contributing to a more reliable and efficient security infrastructure. This proactive approach allows organizations to bolster their defenses and respond more effectively to an ever-evolving threat landscape.

A significant challenge in cybersecurity is the inherent class imbalance within datasets – the disproportionate representation of normal network traffic versus malicious attacks, and, crucially, the scarcity of data representing novel or rare attack vectors. This imbalance severely hinders the performance of machine learning models, which often prioritize recognizing frequent patterns while overlooking critical, yet infrequent, threats. By strategically augmenting training data with synthetically generated examples of these rare attacks, models gain the necessary exposure to learn their characteristics. This process effectively rebalances the dataset, allowing algorithms to move beyond simply recognizing common threats and develop the capacity to identify and respond to sophisticated, low-frequency attacks that might otherwise go undetected, ultimately bolstering overall security posture.

Evaluations reveal a robust overall performance of the synthetic data-augmented security model, achieving a $77\%$ Macro Average F1-score. This metric signifies a strong ability to balance precision and recall across all attack classes. Notably, the model demonstrates perfect classification – a $1.00$ F1-score – for common attack types represented by Class 0 and Class 1. This indicates the generated synthetic data effectively enhanced the model’s capacity to identify and respond to frequently observed threats, suggesting a successful replication of realistic characteristics for these prevalent attack scenarios and a valuable contribution to bolstering defenses against common cyber threats.

Despite overall improvements in cybersecurity model performance through synthetic data augmentation, a critical limitation emerged in the accurate identification of rare attack types. Analysis revealed a $0.00$ F1-score for Class 4 attacks, indicating the model consistently failed to recognize these infrequent but potentially devastating threats. This deficiency stems from challenges in generating synthetic samples that adequately represent the characteristics of minority classes, effectively creating a training bias. While the system excelled at detecting common attack vectors, its inability to generalize to less frequent scenarios underscores the need for advanced techniques capable of producing more diverse and representative synthetic datasets, particularly for underrepresented attack classes, to bolster comprehensive security coverage.

Synthetic data offers a powerful mechanism for proactively evaluating the robustness of cybersecurity infrastructure. Rather than relying solely on real-world attack simulations or passively waiting for breaches, organizations can subject their systems to a controlled deluge of artificially generated threat scenarios. This stress-testing approach allows security teams to identify weaknesses in defenses, pinpoint configuration errors, and assess the performance of intrusion detection systems under extreme conditions. By simulating a wide range of attack vectors – from common exploits to novel, zero-day threats – organizations can gain valuable insights into their security posture and bolster resilience before facing real-world adversaries. The ability to fine-tune the characteristics of the synthetic attacks – such as volume, frequency, and complexity – provides a level of control and repeatability that is difficult to achieve with traditional testing methods, ultimately leading to more effective and adaptable security systems.

The development of PHANTOM represents a significant shift towards preemptive cybersecurity strategies. Rather than solely reacting to detected intrusions, organizations can now leverage synthetically generated data to anticipate and neutralize threats before they materialize. This adaptive approach allows security systems to continuously learn and evolve, mirroring the dynamic nature of modern cyberattacks. By proactively identifying vulnerabilities through stress-testing with synthetic data, and by fortifying defenses against rare attack vectors, PHANTOM enables a resilience that extends beyond traditional reactive measures, ultimately positioning organizations to not merely withstand, but to stay ahead of, the ever-changing landscape of digital threats.

Classification performance on synthetic data closely mirrors results on a real test set, as indicated by consistent true positive, false positive, and false negative rates.
Classification performance on synthetic data closely mirrors results on a real test set, as indicated by consistent true positive, false positive, and false negative rates.

The presented framework, PHANTOM, embodies a holistic approach to a persistent challenge in cybersecurity: the scarcity of labeled data. It recognizes that simply adding more data isn’t enough; the quality and representativeness of that data are paramount. This resonates with the insight of Henri Poincaré: “It is through science that we arrive at truth, but it is through simplicity that we arrive at clarity.” PHANTOM doesn’t attempt to replicate the complexity of real-world attacks directly, but rather leverages the interplay between GANs and VAEs to generate high-fidelity synthetic data, effectively distilling the essence of potential threats. This simplification, while deliberate, aims to enhance the performance of intrusion detection systems by addressing the inherent limitations of imbalanced datasets and providing a more robust foundation for accurate threat modeling.

Beyond the Mirage

The pursuit of synthetic data, as exemplified by PHANTOM, reveals a fundamental tension. The desire for increasingly realistic simulations often leads to architectural complexity – a brittle elegance. While GANs and VAEs offer a powerful toolkit for data augmentation, the true measure of success will not be fidelity alone, but robustness. A system built on exquisitely detailed, yet ultimately artificial, patterns risks failing catastrophically when confronted with the unpredictable nature of genuine threats. The field must resist the temptation to endlessly chase photorealism; instead, it should prioritize the generation of data that exposes the weaknesses of detection systems, not simply their strengths.

A crucial, and often overlooked, aspect remains the inherent bias within the generative models themselves. PHANTOM, like any data-driven approach, inherits the limitations of its training data. The creation of truly novel attack vectors – those unseen during training – demands a shift in perspective. Perhaps the most fruitful avenue lies not in mimicking existing attacks, but in exploring the space of all possible attacks, guided by principles of information theory and game theory. A simpler model, capable of generating minimal, yet effective, adversarial examples, may ultimately prove more resilient than a complex system prone to overfitting.

The long game is not about building a perfect simulation, but about creating systems that are fundamentally unpredictable to the attacker. The elegance of a solution, it should be remembered, often lies not in its complexity, but in its ability to achieve a desired outcome with minimal intervention. If a design feels clever, it is probably fragile.

Original article: https://arxiv.org/pdf/2512.15768.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-21 09:27