Could Public Cloud Pose a Security Risk to Autonomous Vehicles?
I work with IT and business leaders in the auto industry every day, and they’re constantly thinking about what’s next, what new services they’ll need to offer, and how autonomous vehicles will fundamentally change the way they build cars. One of the biggest changes will be the sheer amount of data that autonomous vehicles produce.
By 2030, it’s predicted that each vehicle could be generating up to 10 terabytes per day, or one zettabyte across the whole industry. For comparison, that’s about the same amount of data as the entire world’s internet traffic from the whole of 2016. Every single day.
That means automakers and OEMs will no longer be just manufacturing companies – they will be software companies with a manufacturing arm. Today, automakers are in the process of building the IT infrastructure they need for that autonomous future, where data analysis with machine learning will be one of their most important business functions.
It’s an emerging market, so every company wants to gain first-mover advantage. Many are building new platforms and applications on third-party algorithms, rather than going through the time-consuming process of coding the software in-house.
But there is a very big risk here. Using open-source or third-party algorithms may fundamentally undermine the safety and compliance of autonomous vehicles. It could leave vehicles vulnerable to dangerous accidents or malicious cyberattacks by hackers. In either case, these dangers may cost manufacturers millions in damages or, at worst, put the lives of passengers at risk. It’s an issue which is not yet being talked about widely enough in the industry – and it’s one that IT and engineering leaders need to start thinking about today.
Algorithms are widely available in the public cloud today. They provide the foundation for many emerging AI and machine learning use cases. They allow companies of all shapes and sizes to benefit from intelligent data analysis. But the strengths of these algorithms – simplicity and accessibility – could also be weaknesses.
Public cloud algorithms are developed in a black box, giving users little insight into how they have been implemented. And even if they did, machine learning code can run to hundreds of thousands of lines, which data scientists in manufacturing companies simply don’t have the time, resources or expertise to review. So automakers are currently building software that uses third-party algorithms, without understanding the mathematical formulas in detail.
This year, it was revealed that ‘typographic attacks’ could confuse object recognition software run by neural networks. The AI could be fooled by simply mislabeling objects with a sticker or by adding some noise. And this raises serious concerns, especially when the faults directly impact safety-critical functionalities in the vehicles.
It’s not uncommon to see damaged or graffitied street signs in urban areas. For a human driver, it’s straightforward to filter out the unnecessary bits and make sense of the instructions – but it’s not so easy for an algorithm today. Software built on imperfect public domain algorithms might struggle to interpret crucial road signals, such as traffic lights or stop signs. But more worryingly, they may be at risk of adversarial attacks from hackers, who could endanger passengers and vulnerable road users with physical signals that would cause their vehicle to malfunction.
These risks might seem distant, but any vehicles which incorporate algorithms from the public cloud may also be importing their weaknesses too. It could fatally undermine your vehicle model’s viability in the market.
