Driverless cars may be a thing of the future, but connected cars aren’t, so the entire automotive information security ecosystem has to be locked down, says PAUL WILLIAMS, Fortinet country manager for SADC.
Driverless cars, now being tested on public roads in countries such as the United Kingdom, France, and Switzerland, may be a futuristic dream in South Africa. But connected cars with inbuilt intelligence, and digitally-enabled public transport, are already here; presenting multiple potential security risks to motorists, manufacturers and automotive partners.
On the road to the intelligent driverless car of the future, we are already connecting vehicles and equipping them with a range of intelligent features. These connected, intelligent systems gather potentially sensitive information and communicate it with a control or command centre. Point of sale information, entertainment and online services delivered within the vehicle have to be secured. As we advance toward fully autonomous vehicles, controls including steering, braking, engine management and navigation will depend on a fully secure ecosystem supported by a reliable 3G/4G/5G and Carrier Wi-Fi connection, to function safely.
Effectively securing this ecosystem will depend on close collaboration between vehicle manufacturers, application developers, service providers and carriers. In South Africa, achieving self-driving cars will also depend on expanded Mobile or Wireless coverage across towns, cities and the country. Efforts are already being made internationally for automotive, IT and security stakeholders to work together more closely to ensure a fully secure environment for self-driving and connected cars, but their efforts will have to intensify as the pace of smart car development picks up.
Incorporating more and more technology into a vehicle, whether for improving the customer’s driving experience or enhancing the vehicle’s performance, must be balanced with the management of their potential threats and risks. Ensuring that appropriate and effective security technologies are implemented within these systems must be a mandatory objective, even if it’s not (yet) a regulatory requirement.
Additionally, a growing problem with many IoT devices is that they use common communications programs that have no security built into them at all. As a direct result, an alarming number of IoT devices to date have been highly insecure. We need to achieve better for autonomous cars than what is the current IoT benchmark today.
At the same time, manufacturers must work with their different technology and communications suppliers, across all of the territories where their vehicles are sold, to ensure that any network connections to the vehicles are appropriately hardened.
Automotive security can be addressed as three distinct domains that may make use of similar techniques in some instances, and require novel treatments in others.
- Intra-vehicle communications. Smart vehicles will have several distinct on-board systems, such as vehicle controls systems, entertainment systems, passenger networking, and even third-party systems loaded on-demand by owners. To a certain extent, these systems will need to engage in “cross-talk” to bring new services to life, but this cross-talk needs to be closely monitored and managed by systems such as firewalls and Intrusion Prevention Systems (IPS) that can distinguish between legitimate and normal communications and illicit activity in the car’s area network.
- External communications. Many, if not all on-board systems will have reasons to communicate to Internet-based services: for manufacturer maintenance, for software updates, for passenger Internet access, for travel and driving instructions, for service requests, to purchase items or services, or to backup data. External communications will very likely be both “push” and “pull” – they may be initiated either from inside the vehicle, or to the vehicle from a manufacturer or the Internet. This also means that traffic to and from the vehicle will need to be inspected and managed for threats and illicit, defective, or unauthorized communications using firewalls and IPS-like capabilities.
- Next, the connectivity infrastructure used by a vehicle will likely be based on well-established cellular networks, such as 3G/4G/5G and Carrier Wi-Fi data services, but with a twist. While these wireless services already provide connectivity to billions of smart phones and other devices around the world today, they also suffer from inconsistent security. Smart, driver-assisted, or even driverless vehicles will raise the stakes significantly. A directed attack on or through the “connected” network could trigger significant, safety-critical failures on literally thousands of moving vehicles at the same time. Securing “the connected” networks providing critical vehicle communication will require a thorough review in light of such potential catastrophe.
- Finally, high-assurance identity and access control systems suitable and designed for machines, not people, will need to be incorporated such that: cars can authenticate incoming connections to critical systems, and internet-based services can positively and irrefutably authenticate cars and the information they log to the cloud, or transaction requests they may perform on behalf of owners – such as service requests or transactions to buy fuel or pay tolls.
Unless efforts are stepped up to secure the entire automotive environment, Gartner’s vision of driverless vehicles representing approximately 25 percent of the passenger vehicle population in use in mature markets by 2030 will be fraught with new risks.
From a hacker’s perspective, connected and driverless cars will represent yet another opportunity to wreak havoc by remotely accessing a vehicle and compromising one of its onboard systems, resulting in a range of risks from privacy and commercial data theft, to actual physical risks to people and property.
Here are some attacks that are likely to be targeted at highly connected and autonomous cars:
Privilege escalation and system interdependencies: not all systems and in-car networks will be created the same. Attackers will seek vulnerabilities is lesser-defended services, such as entertainment systems, and try to “leap” across intra-car networks to more sensitive systems through the integrated car communications systems. For instance, a limited amount of communication is typically allowed between an engine management system and an entertainment system to display alerts (“Engine fault!” or “Cruise Control is Active”) that can potentially be exploited.
System stability and predictability: Conventional, legacy car systems were self contained, and usually came from a single manufacturer. As new autonomous cars are developed, they will very likely need to include software provided by a variety of vendors – including open source software. Information technology (IT), unlike industrial controls systems such as legacy car systems, are not known for predictability. IT systems, in fact, tend to fail in unpredictable manners. This may be tolerable if it is just a matter of a web site going down until a server re-boots. It is less acceptable in the event of a guidance systems being degraded even slightly when an adjacent entertainment or in-car Wi-Fi systems crashes or hangs.
Also expect to see known threats be adapted to this new target, expanding from common Internet platforms like laptops and smart phones an IoT device like an autonomous car. For instance:
Botnet Attack: The Botnet “robot” attack is on the increase to an extent of the endpoint is now becoming the victim, without them realizing the attack at first. This attack can be targeted to a single endpoint or a handful of machines, network and endpoints simultaneously, depending the severity of the attack. The infection takes place normally through malware, with a specific Trojan viruses which allows the cybercriminal to start controlling the environment. The answer is to ensure an Application control function, Botnet detection with IP Reputation and Distributed Denial of Service (DDoS) system is in place to monitor and defend against such attacks. If the driverless car is receiving email type messages or the same type of format, nothing stops this way of communication being compromise.
Ransomware: Ransomware is certainly on the rise on PCs and mobile phones. But driverless cars represent an almost ideal target. Imagine the following scenario: a hacker uses the in-car display to inform the driver that his car has been immobilized and that a ransom must be paid to restore the vehicle to normal operation. While a laptop or tablet may be restored relatively easily with potentially no damage, assuming backups are available, a car is a very different story. The owner may be far from home (the ransomware could be programmed to only launch when the car is a predetermined distance from its home base.) Naturally, few dealerships would be familiar with resolving this sort of problem, and specialist help would most likely be required to reset affected components. The cost of such a ransom is expected to be very high, and will likely take time. In the meantime, the vehicle may have to be towed. So the question is, what is the amount of the ransom demand that we expect to see? Estimates are that it is likely to be significantly higher than for traditional computer ransomware, but probably less than any related repair costs so that the car owner is tempted to pay.
Spyware: Perhaps a more attractive target for hackers is collecting data about you through your car. Driverless cars collect massive amounts of data, and know a lot about you – including your favourite destinations, your travel routes, where you live, how and where you buy things, and even the people you travel with. Imagine a hacker, knowing that you’re travelling far from home, sells that information to a criminal gang who then breaks into your home, or uses your online credentials to empty your bank account.
That last risk exists because your driverless and connected vehicle is likely to become a gateway for any number of electronic transactions, such as automatic payment of your daily morning coffee, or parking charges, or even repairs. With sensitive information stored in the car, it becomes another attack vector to obtain your personal information. And with RFIDs and Near Field Communications (NFC) becoming commonplace in payment cards, accessing their details through your car would be another way to capture data about you and your passengers.
And last but not least, there are legal and authenticity issues. Can we consider the location data of the car as authentic? That is, if your car reports you opened it, entered it, and travelled to a particular location at a certain time of the day, can we really assume everything happened as recorded? Will such data hold up in court? Or can this sort of data be manipulated? This is an issue that will need to be addressed. Similarly, if cars contain software from several different providers, and spends the day moving from one network to another, who is accountable or liable for a security breech and resulting losses or damage? Was it a software flaw? Was it negligent network management? Was it on-board user-error or lack of training?
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.