Fast Flux service networks (FFSNs) are used by adversaries to achieve a high resilient technique for their malicious servers while keeping them hidden from direct access. In this technique, a large number of botnet machines, that are known as flux agents, work as proxies to relay the traffic between end users and a malicious mothership server which is controlled by an adversary. Various mechanisms have been proposed for detecting FFSNs. Such mechanisms depend on collecting a large amount of DNS traffic traces and require a considerable amount of time to identify fast flux domains. In this paper, we propose an efficient AI-based online fast flux detection system that performs highly accurate and extremely fast detection of fast flux domains. The proposed system, called PASSVM, is based on features that are associated with DNS response messages of a given domain name. The approach relies on features that are stored in two local databases, in addition to features that are extracted from the response DNS messages itself. The information in the databases are obtained from Censys search engine and IP Geolocation service. PASSVM is evaluated using three types of artificial neural networks which are: Multilayer Perceptron (MLP), Radial Basis Function Network (RBF), and Support Vector Machines (SVM). Results show that SVM with RBF kernel outperformed the other two methods with an accuracy of 99.557% and a detection time of less than 18 ms.