Developing Autonomous Cyber Defense Systems Using Generative Adversarial Networks for Anomaly Detection in IoT Data Streams

The proliferation of Internet of Things (IoT) devices in critical sectors such as healthcare, energy, and smart infrastructure has significantly expanded the digital attack surface, exposing systems to increasingly sophisticated cyber threats. Traditional rule-based and static anomaly detection methods often fail to identify subtle or zero-day attacks in real time due to the sheer volume and heterogeneity of IoT data streams. This paper proposes a novel approach to cyber defense through the development of autonomous anomaly detection systems powered by Generative Adversarial Networks (GANs), enabling real-time identification of malicious behaviors in high-velocity IoT environments. From a broader perspective, the research contextualizes the limitations of current machine learning-based security models in handling complex, dynamic, and adversarial data patterns common in distributed IoT ecosystems. Narrowing the focus, the study introduces a GAN-based architecture wherein the generator simulates normal IoT traffic patterns, and the discriminator learns to distinguish between authentic and anomalous data behaviors. This adversarial training improves the system’s sensitivity to minute deviations without prior knowledge of attack signatures. The framework integrates edge computing nodes to enable low-latency detection while preserving bandwidth and computational resources. Extensive experiments on publicly available IoT datasets demonstrate that the proposed system achieves superior performance in detecting both known and unknown threats, with significantly reduced false positive rates compared to conventional techniques. Moreover, the adaptability of GANs allows the system to continuously learn and evolve in response to new attack vectors, thereby supporting autonomous cyber defense. This paper concludes by discussing deployment strategies, integration with SIEM platforms, and future directions for GAN-based cybersecurity in IoT-centric digital infrastructures.