We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics. The IoT is imminent – and so are the security challenges it will inevitably bring. Get up to speed on IoT security basics and learn how to devise your own IoT security strategy in our new e-guide.
Amir Hever was driving into a government facility a few years ago when he discovered a huge flaw in their security process. As he approached the entrance gate, a security guard dropped to his knees to look underneath his vehicle. "When he stood up, I asked him what he was looking for," said Hever, CEO and co-founder of computer vision startup UVeye. "The security guard answered honestly that he was looking for threats but actually couldn't see anything. That's when I realized that something wasn't working right."
Should you be worried about a car being hacked? USA TODAY's Jefferson Graham and Chris Woodyard break it down. Model cars run in a city miniature at the Elektrobit booth to show how software for highly automated driving works during CES 2018 in Las Vegas. Automakers and suppliers are making progress in protecting vehicles from cyber attacks, but the car-hacking threat is still real and could get increasingly serious in the future when driverless vehicles begin talking to each other. A worst-case scenario would be hackers infiltrating a vehicle through a minor device, such as an infotainment system, then wreaking havoc by taking control of the vehicle's door locks, brakes, engine or even semi-autonomous driving features.
The US state police in Delaware is preparing to deploy "smart" cameras in its vehicles to help its officers detect a vehicle carrying a fugitive, a missing child or a disoriented senior. David Hinojosa of Coban Technologies, the company providing the equipment, explained that the video streams will be analyzed using artificial intelligence to identify vehicles by license plate or other features to "give eyes additional "to patrol agents. "We are helping agents stay focused on their work," said Hinojosa, who calls the new technology a "steroid-embedded camera." Nowadays, more and more companies are offering computer-aided vision technologies. We can mention the Israeli start-up Briefcam, which uses artificial intelligence to interpret video surveillance sequences.
Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems.
Most of us will know the age old saying, we want to be "safe and secure" – that's ourselves, our family, and our work colleagues in all aspects of life. However, our understanding of what it means to be safe and secure, especially when considering today's modern digital age and in particular the growing era of the Internet of Things (IoT), isn't the same as it once was. For sure, the natural evolution of innovation, technological or otherwise, continues irrespective of the accelerating awareness and adoption of the interconnection of consumer and industrial devices which makes up the IoT. The world of Industrial Internet of Things (IIoT) is also evolving at a similar pace and now more than ever is bridging into consumers' lives on an individual level. So much so that it is becoming more difficult to differentiate the IoT from IIoT, outside of those in the industry of course.
Emerging technologies are finding their way into everyday life as they mature and gain acceptance. But that doesn't mean they're without risk. The CERT Division of the Software Engineering Institute at Carnegie Mellon University once again has updated its list of technologies that might present challenges from an information security and safety perspective. In its Emerging Technology Domains Risk Survey, CERT examines a variety of trends that can provide a lot of benefits to people and businesses, but also pose risks that need to be addressed. Some of these areas are moving ahead so quickly in adoption that companies have not had a chance to completely evaluate their implications.
Issues around data ownership and security, safety ethics, legal liability and insurance, and the future of employment need to be addressed before Australians will be comfortable with automated vehicles, the House of Representatives Industry, Innovations, Science and Resources Committee has said. After conducting a six-month enquiry into the social issues relating to land-based automated vehicles, the committee has announced its 10 recommendations, with an emphasis on data security. The Industry, Innovations, Science and Resources Committee also recommended continued funding of automated vehicle trials with a public transport application in both metropolitan areas and regional locations. The national body or cross-agency taskforce, once established, would address concerns around data security, safety ethics, legal liability and insurance, transport accessibility, and the impact of automated vehicles on employment.
Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). When hackers demonstrated that vehicles on the roads were vulnerable to several specific security threats, automakers responded by recalling and upgrading the firmware of millions of cars. The computer vision and collision avoidance systems under development for autonomous vehicles rely on complex machine-learning algorithms that are not well understood, even by the companies that rely on them (see "The Dark Secret at the Heart of AI").