Wireless networks such as IEEE 802.11 networks are nowa-days widely used. However, they are vulnerable to different forms of attacks such as the fake access point and the emis-sion of intentional frequency sweeping jamming signals. In this paper, we present a Network-based Intrusion Detection System (NIDS) which uses a threshold-based approach to detect the two attacks on IEEE 802.11 networks. The NIDS analyses indicators derived from frame header information to determine the presence of the two attacks. We also dis-cuss the limitations of this approach and give perspective for a new line of research based on a supervised learning model approach.