Pervasive computing is being applied to different areas of specialization. This is basicallyrnbecause of the features of pervasive computing like context-awareness, invisibility, nonintrusiveness,rnand mobility. The medical area is one where such devices are hugely deployed. Inrnthis case, the pervasive devices; PDA (Personal Digital Assistant), mobile phones and the like,rnare used for manipulating medical records on the move.rnThe use of pervasive devices also comes with new challenges that did not exist with traditionalrncomputing systems. Among these challenges, security is probably the major one. In fact,rninsuring security with pervasive systems is difficult due to the use of wireless communication,rnthe physical nature and the low processing and low power nature of the devices.rnIn this research, we deal with intrusion detection, ID, to secure such systems. ID Systems, IDSs,rnare used to monitor a resource and notify someone in the event of a specific occurrence for anrnappropriate response. Based on attack identification, they can be those which implement misuserndetection, matching against known attack patterns, and those which implement anomalyrndetection, deviation from normal patterns. Misuse detection is used for matching only knownrnpatterns of attacks while anomaly detection is capable of identifying new attacks by matchingrnwith an already established normal profile. Based on source of information for the IDS, it mayrnbe host-based, network-based or application-based. For our case, we deal with application basedrnanomaly detection modeling issues through building normal usersâ€™ application usage profiles