I'm currently working on access control project, Smart Lock to be more spesific. Like the other smart lock system, the system required user's authentication to open the door. I'm using RFID as authentication method.
to make my product be more secure, i want to equip my system with 'Machine Learning' to learn the user's behaviour on unlocking the door. shortly, the system will store user's history when unlocking the door (Timestamp). with those data, the system will recognize the patterns of user so it will identify everytime user open the door whether it is normal or anomaly.
for example if the user is usually open the door at 6 a.m and 6 p.m but at one point the system detects there is an attempt to open the door at the middle of the night, it will considered as an anomaly.
i've been reading any literature and realizing that to resolve this case, i have to use unsupervised learning for clustering and they said that K-Means is suitable for clustering. but my question is how to use K-Means if my data is only timestamp?