System call frequency analysis-based generative adversarial network model for zero-day detection on mobile devices

Abstrak

In today's digital age, mobile applications have become essential in connecting people from diverse domains. They play a crucial role in enabling communication, facilitating business transactions, and providing access to a range of services. Mobile communication is widespread due to its portability and ease of use, with an increasing number of mobile devices projected to reach 18.22 billion by the end of 2025. However, this convenience comes at a cost, as cybercriminals are constantly looking for ways to exploit security vulnerabilities in mobile applications. Among the several varieties of malicious applications, zero-day malware is particularly dangerous since it cannot be removed by antivirus software. To detect zero-day Android malware, this paper introduces a novel approach based on generative adversarial networks (GANs), which generates new frequencies of feature vectors from system calls. In the proposed approach, the generator is fed with a mixture of real samples and noise, and then trained to create new samples, while the discriminator model aims to classify these samples as either real or fake. We assess the performance of our model through different measures, including loss functions, the Frechet Inception distance, and the inception score evaluation metrics.