An immune-based risk assessment method for digital virtual assets

摘要

Digital virtual assets are playing an increasingly important role and have become an indispensable part of people's lives. However, due to the characteristics of network, virtuality, and openness, digital virtual assets are extremely vulnerable to attack. Meanwhile, it is difficult to guarantee the security of digital virtual assets based on advanced encryption technology and game theory-based asset security recognition and protection methods. Therefore, how to identify the possible attacks of digital virtual assets and timely accurate assessment the risks are the urgent problems to be solved. In this paper, we design an immune-based risk assessment method for digital virtual assets by simulating the mechanism of the human immune system "synchronous dynamic evolution of antibody concentration with invasion virus". First, we propose a negative selection algorithm based on the "Designated + Random" mode by hierarchical division (HD-NSA), which can efficiently generate high-performance immune detectors to identify attack risks. Then a threat risk assessment model for digital virtual assets by simulating the concentration change of antibody in the immune system is established to assess the risk of attacks. Experiments on bitcoin dusting attack show that, the method proposed in this paper can detect attacks more quickly and accurately, and can also assess the risk of different users being attacked in real time.