Mobile ad hoc network (MANET) is one of the common types of wireless network that can be operated without the support of fixed infrastructure. It has dynamic topology and routing information is based on the hop by hop approach. MANETs are subjected to numerous attacks than wired networks as its routing protocols are limited in security. Since MANETs are used on the battlefield or physically inaccessible areas, attack detection is a very substantial task. Network layer attack is one of the most serious attacks that can affect MANET performance by which a node illegally uses network resources. Motivated by this fact, this thesis focuses on the development of network layer attack detection system for MANETs. The work in this thesis is carried out in two phases. The first phase of the work focuses on dataset preparation. In this phase, data is collected by running simulations with both normal and malicious behavior of mobile nodes using the Ad-hoc on-demand distance vector (AODV) routing protocol. This was done by using Ns2 (network simulator version 2). After preprocessing and analyzing the raw data, a total of 14150 dataset records with sixteen features are generated from the trace file. The second phase of the work is related to the development and evaluation of the proposed attack detection system. For detecting network layer attacks, the proposed system has been trained and evaluated using supervised machine learning algorithms (Support Vector Machine and Random Forest). From the experiments, it is found that the detection rate of the proposed attack detection system using Random Forest and Support Vector Machine algorithms was 99.87% and 99.25% respectively. The results recorded for accuracy, precision and F-score were not only an important indicator of the quality of our dataset but also confirm the effectiveness of the proposed attack detection method.