Symantec Corp, introduced Symantec Anomaly Detection for Automotive to protect against zero-day attacks and 
never-before-seen issues facing modern connected vehicles. Bringing Symantec’s extensive security and sophisticated analytics expertise across complex networks to the vehicle, Anomaly Detection for Automotive provides the crucial ability to identify issues for early remediation.
Connected cars offer drivers conveniences such as navigation, remote roadside assistance and mobile internet hot spots. There will be 220 million connected cars on the road in 2020, according to Gartner.1 While new technologies promise to enhance the driving experience, these advancements also create avenues of attack for hackers that can endanger drivers and passengers.
Symantec Anomaly Detection for Automotive uses machine learning to provide passive in-vehicle security analytics that monitor all Controller Area Network (CAN) bus traffic without disrupting vehicle operations, learn what normal behavior is and flag anomalous activity that may indicate an attack. The solution works with virtually any automotive make and model.
“The Internet of Things contains many different areas, but connected automobiles will radically alter transportation and mobile communications,” said Christian Christiansen, IDC VP of Security Products. “As connected automobiles become the norm, security issues have already drawn attention. Driven by opportunity, manufacturers and their suppliers will partner with cybersecurity vendors on securing connected cars as they would with any other networked endpoint such as a mobile devices and laptops. Keeping security top of mind will not only help ensure the safety of drivers and passengers but also build trust in the car manufactures and the overall Internet of Things ecosystem.”
Key benefits of Symantec Anomaly Detection for Automotive include:
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Learning the vehicle’s behavior in a deeper, more precise way, enabling automakers to see previously unseen attacks.
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Automatically prioritizing incidents based on perceived criticality and risk.
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Automatically detecting anomalies without requiring manufacturers to set rules or create policies.
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Using minimal memory and CPU power with an analytics solution built from the ground up for vehicles.
