Cybersecurity for the Automotive Industry


Vol. 16 // 2021

Neurone network, copyright iLexx via Creative Market


Cars are an integral part of modern society, but they are not without their issues. Maintenance problems and breakdowns ranging in severity from inconvenient to dangerous have long plagued car owners. Now, as cars become more computerized and connected, hacking poses a problem as well.

With the advent of machine learning, these issues can instead be an opportunity for companies in the automotive industry. By marrying AI technologies for cybersecurity and predictive main – tenance, automotive manufacturers and sellers can offer their customers a unified solution to car troubles both new and old.


In 2015, researchers Charlie Miller and Chris Valasek made headlines by using a zero-day exploit to remotely hack into a moving Jeep on a highway. They were able to take complete control over the vehicle, manipulating everything from steering to the radio[1].

Since then, more and more information has come out of the woodwork about potential security flaws in computerized cars– and the ways in which these flaws could be exploited. When cars run on a computer OS, they are vulnerable to the same malware, worms, and ransomware as any other endpoint. Consumers are increasingly concerned about these security flaws, and rightly so.

By monitoring system and app data transmitted from the car’s automotive control system, cognitive cybersecurity solutions detect and disrupt malware and raise alerts for car owners and manufacturers.

DeepArmorTM has trained on millions of malicious and benign files, and provides signature-free, out-of-band, symptom-based endpoint security that protects vehicles from takeover, espionage, and other forms of hacking.

Neurone network, copyright iLexx via Creative Market


Any machinery used as frequently as the car is bound to run into failures and breakdowns. However, the consequences of failures in cars are steep. Motorists whose cars unexpectedly malfunction may wind up stranded on the side of the road for extended periods of time. Car problems are expensive, with even brand-new vehicles costing an average of $1,186 per year to main – tain and repair[2]. And of course, car troubles can lead to danger – ous accidents–there were 37,461 fatalities from crashes in the US in 2016 alone[3].

AI conditions-based health monitoring software on a car reads pipelines of sensor data from the automotive control system to detect anomalies and predict failures in advance. SparkPredictTM provides immediate insight to car owners, dealers, and engineers of health issues for cars, enhancing customer product satisfaction and reducing warranty servicing and maintenance time and costs.

Using SparkPredictTM, manufacturers can anticipate potential recall issues using collective data. Drivers, on the other hand, are provided a custom maintenance schedule based on driving pat – terns, use, and parts inventory at local repair shops. Engineers and repair workers are better able to pinpoint the exact source of any car troubles.


The joint usage of cognitive security and predictive analytics creates a whole new level of service and safety for automotive manufacturers and dealers to offer their customers, whether these embedded in cars as part of a warranty, as an optional ad – don, or simply as a differentiator in a competitive market. In this way, companies in the automotive industry can enhance their brand and reputation while providing complete protection for consumers.


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