The connectivity of mobile networks is increasing heavily and the evolution of the risks is highly dynamic. In Mobile Ad Hoc Network (MANET), attacks and digital attacks are becoming increasingly complex. Due to their nature, these networks make some information unavailable and/or incomplete needed for attacks detection process. Several solutions have been made to ensure the security of mobile networks specially intrusion detection systems (IDS). This solution allows enhancing IDS detection efficiency even with incomplete information about occurred attacks. In this paper, we propose an IDS based on three algorithms NCF, FNF and DPA allowing special traffic abstraction and data collection. We used these algorithms to generate a "behavioral database" for supervised nodes in the network. We study and implement four types of Denial of Service (DoS) attacks, which could disturb the routing process in MANET. These attacks are Blackhole, Grayhole, Wormhole, and Flooding attack. We generate these types of attacks by modifying the normal AODV routing algorithm behavior. We have implemented these attacks using Opnet Modeler 14.5. We proposed a set of IDS nodes to supervise the network behavior using Fuzzy Inference System (FIS). These nodes identify a pattern for each attack behavior to be stored in the "behavioral database". The performance of a network under attack is investigated.