An Intrusion Detection Model Based on Improved ACGAN in Big Data Environment

Abstract

With the development of big data technology, network intrusion problems against server vulnerabilities emerge one after another. To improve the accuracy of intrusion detection, this paper designs an intrusion detection platform based on the ACGAN (auxiliary classifier generative adversarial network) model in a big data environment. Firstly, by introducing a self-attention mechanism, the global characteristics of attack samples are extracted to improve the quality of generated samples. Then, by adding a gradient penalty, the model's convergence speed and training stability are improved. Finally, this method enhances and expands the attack samples and verifies the dataset. The experimental results show that compared with other comparison methods, the overall detection accuracy of this system is higher, and the false-positive rate and false-negative rate are lower.