If you’ve watched a product launch from any of the big technology giants in the past two years, you would be hard-pressed not to have heard of machine learning.
In addition to Artificial Reality or Augmented Reality, it’s currently one of the biggest buzzwords in the IT world.
Machine learning has the power in future to add value to the physical security function by providing greater insight into what is going on within our organisations.
What exactly is machine learning in physical security?
According to Wikipedia, Machine learning is a field of computer science where computers have the ability to learn without being given explicit instructions or programming.
In a very broad sense, this means that computers can think for themselves.
What can machine learning in physical security do for me?
Rather than this leading to computer operated drones replacing security staff in a George Orwell envisioned future, I see machine learning being able to interpret and analyse data contained within physical security systems.
Historical Pattern Analysis
Imagine an organisation where most employees start and end their workday consistently within a 1-2 hour time range each day.
Two employees suddenly start coming into work much earlier and then leaving at the same time as usual.
This could be completely normal, perhaps due to a seasonal increase in workload, or working on a project with a client in another time-zone which requires them to be in early.
This could also be an indicator that they are up to no good and are trying to avoid being watched.
By analysing stored data within the access control system with machine learning, we can accurately predict peak flows of traffic.
This data can be used to drive electronic signage to ensure that traffic can be managed throughout the day.
Imagine if that organisation had a theoretical maximum of four lanes connecting the site to the motorway.
If we know at 0800, most employees arrive, we can switch multiple lanes to allow employees to gain entry.
Once employees have started work, lanes can then be re-allocated to visitors and delivery vehicles.
This reduces the impact on the site, and surrounding public roads to avoid queueing and traffic disruption.
Cleaners will typically check toilet facilities every two hours to they are clean and supplies are re-stocked.
In a large building or estate, this process can be very resource intensive, and therefore expensive.
It’s possible that in a large building, some toilets won’t be used at all, let alone since the last check.
By fitting a door contact to the main entrance door, we can count how many times they are used.
Cleaners can use this information to prioritise which toilets they clean and check in which order.
Incorporating Internet of Things technologies, similar to Amazon Dash Buttons, where building users can press a button when an area is out of soap or toilet paper, cleaners could also top up supplies on demand.
Whilst most of these technologies and concepts are possible, they are yet to be introduced in a physical security system.
One thing is for sure, the future applications of machine learning is very exciting!