Nowadays, new flaws or vulnerabilities are frequently discovered. Analyzing how these vulnerabilities can be used by attackers to gain access to different parts of a network allows to provide better protection and defense. Amongst the diverse analysis techniques, simulations do not necessitate a full infrastructure deployment and recently benefited from advances in reinforcement learning to better mimic an attacker's behavior. However, such simulations are resource consuming. By representing the interconnected hosts of a network and their vulnerabilities as attack graphs and leveraging machine learning, our method, Vulnet, is capable to generalize knowledge generated by simulation and gives insight about attacker capabilities. It can predict instantaneously the overall performance of an attacker to compromise a system with a mean error of 0.07.