When BPOD finds that the home is unoccupied, the battery will charge for enhancing the statistical metrics of power usage.
When the battery is full, the raw smart meter data may be exposed, since BPOD cannot inject any power signatures into the demand.
The state will be transformed into state D, when BPOD detects the home is occupied.
In fact, b(t) is related to C(t), [C.sub.H] (the battery's capacity), [M.sub.Threshold] (the power mean thresholds of BPOD), and [[beta].sub.D] (the maximum discharging rate).
In our experiments, we use smart* dataset, a one-second resolution dataset, to measure the performance that BPOD resists various occupancy detection attacks.
Doing so would lead to increase of false positives (detects occupancy and the home is unoccupied) of occupancy detection attacks and decrease of true positives (detects occupancy and the home is occupied), when using BPOD to prevent the attacks.
We also use REDD dataset, a one-second resolution dataset, to verify whether BPOD is able to protect households load signatures.
Since the aim of BPOD is to make a delusion that the home is always occupied, TP + FP is the most important indictor to evaluate the effectiveness that BPOD prevents occupancy detection attacks.
From Table 1, we can also see that both the TP and the FP of the attacks are increased sharply by BPOD. It comes up to what we expect, trying the best to make attackers believe that the home is occupied.
Figure 5(c) clearly manifests that BPOD masks occupancy effectively by injecting appliance consumption signatures when the home is empty or occupants are not operating any electrical devices.
As with other BLH methods, battery capacity is a key factor of BPOD effectiveness.
Though the main aim of BPOD is to prevent occupancy detection attacks, it is also able to thwart the NILM attacks.