Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

Abstract: Software attacks such as worms and viruses are detected in an electronic device by monitoring power consumption patterns. In a first embodiment, software attacks are detected by an increase in power consumption. The increased power consumption can be caused by increased network traffic, or by increased activity in the microprocessor. Monitoring power consumption is particularly effective for detecting DOS/flooding attacks when the electronic device is in an idle state. In a second embodiment, a power consumption signal is converted to the frequency domain (e.g., by fast Fourier transform). The highest amplitude frequencies are identified. Specific software attacks produce characteristic frequencies in the power consumption signal. Software attacks are therefore detected by matching the highest amplitude frequencies with frequencies associated with specific worms and viruses.

Abstract: Software attacks such as worms and viruses are detected in an electronic device by monitoring power consumption patterns. In a first embodiment, software attacks are detected by an increase in power consumption. The increased power consumption can be caused by increased network traffic, or by increased activity in the microprocessor. Monitoring power consumption is particularly effective for detecting DOS/flooding attacks when the electronic device is in an idle state. In a second embodiment, a power consumption signal is converted to the frequency domain (e.g., by fast Fourier transform). The highest amplitude frequencies are identified. Specific software attacks produce characteristic frequencies in the power consumption signal. Software attacks are therefore detected by matching the highest amplitude frequencies with frequencies associated with specific worms and viruses.