User Behavior Threat Detection Based on Adaptive Sliding Window GAN

User behavior threat detection is important for the protection of network system security. Traditional supervised modeling methods and unbalanced sample data lead to a high false positive rate in user behavior detection. In addition, network user behaviors are complex, changeable, and difficult to predict, and existing detection methods are facing ever greater challenges. Effectively detecting user behavior remains a challenge. In this paper, we propose a user behavior threat detection method based on an Adaptive Sliding Window Generative Adversarial Network (ASW-GAN). This method designs an adaptive sliding window mechanism to process behavior data and uses the GAN model to detect threat behavior, finally uses the maximum interclass variance algorithm Otsu to optimize test detection result. Compared with other typical methods, the proposed method achieves a higher accuracy rate and a markedly lower false positive rate, and can effectively evaluate user threat behaviors.