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This paper introduces a supporting model for a unique battery-sensing intrusion protection system (B-SIPS) for mobile computers, which alerts when power changes are detected on small wireless devices. An analytical model is employed to examine the smart battery characteristics to support the theoretical intrusion detection limits and capabilities of B-SIPS. This research explores the modification of the smart battery polling rates in conjunction with the variance of malicious network activity. Using the results from a previous study of optimized static polling rates to create minimum and maximum thresholds, a dynamic polling rate algorithm was devised. This algorithm allowed the smart battery to gauge the network's illicit attack density and adjust its polling rate to efficiently detect attacks, while conserving battery charge life. Lastly, a trace signature methodology is presented that characterizes unique activity for IEEE 802.15.1 (Bluetooth) attack identification.
Buennemeyer et al. (Fri,) studied this question.