The “Fog of War” in cyberspace

Three computer scientists at the U.S. Army Research Laboratory once likened the fight for cybersecurity to the “Fog of War”. This is a term used in uncertain situations by participants in military operations.

Off the back of this, they put forward a new form of data security dubbed “cyberfog”. In comparison to physical military operations, cybercrime can be hard to monitor and detect – who is the enemy? What tools are at their disposal? What are their finances and strengths?

Such uncertainties can make the fight against cybercrime so much harder, so making data harder to hack is always a solid step forward, and that’s essentially what “cyberfog” is intending to do.

Kott, Swami and West came up with the “cyberfog” security idea. This is an approach to split data into many fragments and continually disperse it across multiple end-user devices, which could provide greater resilience to an attack. Effectively, the goal of “fog computing” is to minimise the amount of data that’s transported to the cloud for data processing and storage, among other things.

There are reportedly many security benefits of doing this. It’s clear that companies cannot tackle cyber-crime alone. Industries teaming up with government officials to work together to fend off hackers and ensure security is more than adequate can only ever be a good thing.

How it works

Fog storage is said to break up and store data in a more secure way. Units of information can be broken up into something called “shards” and taken out of their database tables. As such, a malicious hack may only yield these small “shards” of data that probably have a lot less value given the data is out of context with its database tables. When that is taken out of context, a hacker may not have a clue what the data means.

It’s kind of like having a few pieces of a huge puzzle, but with no idea which bits are missing and what the picture of the puzzle should be.

So, fogging is exactly what it says on the tin: it can “cloud” the view of hackers by pulling the data out of context to make it harder for them to understand what the data means.

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