A hacker might use a rogue access point to gain access to a network, this poses athreat to the individuals connected to it. The hacker might have the potential to leakcorporate data or steal private information. The detection of rogue access points istherefore of importance to prevent any damage to both businesses and individuals.Comparing different software that detects rogue access points increases the chanceof someone finding a solution that suits their network. The different type of softwarethat are compared are intrusion detection systems, wireless scanners and a Ciscowireless lan controller. The parameters that are being compared are; cost, compat-ibility, detection capability and implementation difficulty. In order to obtain resultssome of the parameters require testing. As there are three types of software, threeexperiment environments should be conducted. Our research indicates that alreadyexisting network equipment or the size of the network affects the results from theexperiments.

We propose a simple, passive and deployable approach for fingerprinting traffic on the wired side as a solution for three critical stealth attacks in wireless networks. We focus on extracting traces of the 802.11 medium access control (MAC) protocol from the temporal arrival patterns of incoming traffic streams as seen on the wired side, to identify attacker behavior. Attacks addressed include unauthorized access points, selfish behavior at the MAC layer and MAC layer covert timing channels. We employ the Bayesian binning technique as a means of classifying between delay distributions. The scheme requires no change to the 802.11 nodes or protocol, exhibits minimal computational overhead and offers a single point of discovery. We evaluate our model using experiments and simulations.