In the realm of cybersecurity, machine learning emerges as an indispensable tool for advanced threat detection and protection against digital vulnerabilities. GANs, positioned as a potent machine learning paradigm, transcend their traditional role in data generation, showcasing their potential to outsmart detection systems. This chapter sheds light on the evolving challenges posed by GANs in cybersecurity, underscoring the imperative for thorough assessments, especially within the context of intrusion detection systems. While numerous successes characterize various GAN applications, this chapter emphasizes the pressing need to investigate GANs' specific impact on cybersecurity enhancements. GANs not only excel in data generation but also serve as catalysts for novel avenues in privacy and security-oriented research. The chapter concludes by accentuating the limited depth of existing assessments on GANs in privacy and security, urging further exploration to unravel the multifaceted influence of GANs in shaping the future of digital security frameworks.
TopIntroduction
The advent of Generative Adversarial Networks (GANs) has swiftly ushered in a revolutionary era in machine learning and related domains, permeating diverse research areas and applications (Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio, 2014). As a potent generative framework, GANs have significantly propelled advancements in complex tasks, including image generation, super-resolution, and manipulations of textual data (Lotter, Kreiman, Cox, 2015). Recently, the application of GANs to address intricate privacy and security challenges has gained prominence in academic and industrial circles, driven by their game-theoretic optimization strategy. Originally proposed by Goodfellow et al. in 2014, GANs have been hailed as “the most interesting idea in the last 10 years in Machine Learning” by Yann LeCun, the recipient of the 2018 Turing Award. Fundamentally, GANs operate as generative models bridging the gap between supervised and unsupervised learning. In a zero-sum game between the generator and the discriminator, the generator is trained to deceive the discriminator, which, in turn, aims to distinguish real data from generated data. GANs have ushered in a new wave of data-driven applications in the realms of Big Data and Smart Cities, owing to their remarkable properties. Firstly, the design of generative models provides an excellent means to capture high-dimensional probability distributions, a critical focus in mathematics and engineering. Secondly, well-trained generative models mitigate data scarcity, facilitating technical innovation and performance improvement, especially in deep learning. For instance, high-quality generated data can enhance semi-supervised learning, mitigating the impact of missing data to some extent. Thirdly, generative models, particularly GANs, enable learning algorithms to handle multi-modal outputs, accommodating scenarios where a single input may yield more than one correct output for a given task, such as next-frame prediction. Before the advent of GANs, several generative models based on maximum likelihood estimation existed, categorized as either explicit density-based or implicit density-based (Jiang, Zhang, Cai, 2008) (Rabiner, 1989). Explicit density-based models, like Restricted Boltzmann Machine (RBM) and Gaussian Mixture Model (GMM), faced limitations in representing complex, high-dimensional data distributions due to computational tractability issues. GANs, however, overcome these constraints by using a pre-defined low-dimensional latent code, mapping it to the target data dimension (Furthermore, GANs, as non-parametric methods, eliminate the need for approximate distribution or Markov Chain properties, allowing them to represent generated data in a lower dimension with fewer parameters. The architecture of basic GAN is shown in figure 1.
Figure 1. Architecture of basic GAN
The flexibility and extensibility of GANs have led to various variants, including Wasserstein Generative Adversarial Network (WGAN), Information Maximizing Generative Adversarial Network (InfoGAN), and CycleGAN, shown in figure 2., each tailored to specific requirements (Arjovsky, Chintala, Bottou 2027) (Chen, Houthooft, Schulman, Sutskever, Abeel, 2016) (Zhu, Park, Isola, Efros, 2017). Motivated by these characteristics, novel research continues to benefit from the widespread applicability of GANs.