| Publisher | Carleton University | ||
|---|---|---|---|
| Format | 1.8MB PDF | Date added | 01 Sep 2005 |
| Topics | Network Security, Security Tools, Intrusion Detection Systems | ||
| Downloads | 62 | ||
As far as addressing user impersonation, it is feasible to use mobility profiles for Anomaly-Based Intrusion Detection (ABID) in mobile wireless networks. The challenge is to accurately characterize the mobility behavior of users. One simply strategy, which enhances the characterization of users and increases the detection rate at a minimal cost (low percentage of FAs), is to incorporate the missing parameter sequences into the training patterns. Furthermore, the issue of concept drift (accommodating variability in mobility behavior over time) can also be addressed by continuously monitoring the false alarm rate and selectively incorporating newly observed mobility sequences into the training patterns, using a window that is shifted in time (analogous to exponentially weighted moving average).
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