# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 1 Introduction  
The digital age enables both global innovation and an increasing cybercrime economy, which is projected to cost almost 10.5 trillion USD annually by 2025 [1], surpassing the GDP of all but the two biggest countries in the world to become the third largest \"economy\" [2]. This leads not only to enormous financial losses but also to operational damage, decreased public trust, and reputational damage for public and private organizations. A single data breach cost increased by 10% year-over-year to reach 4.88 million USD in 2024 [3]. Moreover, cyberattacks have evolved into sophisticated, continuous threats. More specifically, an estimated 3.4 billion malicious emails circulate daily, with phishing being the dominant attack, accounting for 45.56% of all scam emails in 2021 [4]. Cyberattacks are happening almost every few seconds, whilst an organization needs 258 days on average to contain a data breach [3]. This alarming increase is reflected in IBM's report, which indicated a 266% increase in infostealer deployments and a 71% rise in valid credentialsattacks [5]. Also, emerging technologies like generative Artificial Intelligence (e.g., Large Language Models (LLMs) like ChatGPT) enabled a remarkable increase of 4151% in hyper-personalized phishing and deepfake scams [6], while IoT attacks surged 124% together with a 93% rise in encrypted threats in 2024 [7]. This ever-evolving landscape has turned the attention to the engagement and the development of state-of-the-art solutions that can predict, prevent, tackle, and remedy cyberattacks by engaging smart AI-powered solutions. A significant rise in related scientific research supports this paradigm shift toward AI-driven solutions. According to [PERSON] et al. 's [8] bibliometric analysis, the number of published studies on this topic grew by 1392% over the course of five years, from 13 publications in 2019 to 194 in 2023. There is a clear trend in this growing body of work toward integrative and hybrid approaches. These methods improve detection performance, system resilience, and adaptive capacity by carefully combining several AI techniques, including generative modeling, ensemble classification, and transfer learning. This development recognizes that no single model is always the best approach to counter the various and changing methods utilized by threat actors. AI-driven solutions' performance is highly dependent on data availability and quality. Given this context, data augmentation--whether applied to existing or newly collected datasets--in cybersecurity has gained a lot of interest. Generative Adversarial Networks (GANs), introduced by [PERSON] et al. [9] in 2014, offer significant potential in this area by generating synthetic data records nearly indistinguishable from real input samples. GANs have strong potential to address the ongoing problem of data scarcity of network intrusion records and malware images by generating realistic synthetic samples. These samples can closely resemble real-world occurrences and allow for the simulation of controlled scenarios for uncommon attack types, such as zero-day exploits, in addition to supplementing limited datasets. Dedicated AI solutions are often limited to a very specific task. So, in complex, unbalanced, or noisy operating environments, ensemble methods--combining the predictions of a set of classifiers--consistently exhibit higher accuracy and robustness than single-model approaches. Transfer learning techniques, on the other hand, enable models that have been trained on a single dataset or domain to be successfully adapted for new, related tasks. This improves model generalization across different network infrastructures and significantly reduces the need for extensive retraining. This study presents an integrative ML framework designed to significantly advance cybersecurity threat detection.