Neural Networks Can Help Keep Connected Vehicles Secure

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With everything being connected and software-driven, vehicle tampering will become a real problem. Neural networks can help.

www.eetasia.com/, Sept. 06, 2023 – 

Connected vehicles are, according to the World Economic Forum, forecast to double by 2030, accounting for 96% of all shipped vehicles.

With everything being connected and software-driven, vehicle tampering will become a real problem. Remotely controlling a smart vehicle or interfering with the navigation system can lead to a wide range of safety issues for consumers. In 2021, a new standard called ISO/SAE 21434 was developed in collaboration with SAE International to address the cybersecurity in engineering of electrical and electronic (E/E) systems within road vehicles.

The new standard is designed to ensure high-quality safety and cybersecurity measures throughout the entire product-engineering lifecycle to ensure road vehicles have been designed, manufactured and deployed with security mechanisms to safeguard the confidence, integrity and authenticity of functions in road vehicles.

The connected car experience promises always "on" data-gathering and connectivity, which creates major privacy and data-protection vulnerabilities. So it becomes imperative to safeguard electronics, communications systems, data, software and algorithms against bad actors from intercepting transmitted data content, such as software updates, credit card info, text/phone messages, access to camera videos and other personal/private data.

Neural networks can help.

Neural networks aid in various aspects of data privacy and protection by generating and managing cryptographic keys used in encryption algorithms. By training on a dataset of secure keys, neural networks can learn to generate robust and unpredictable keys, enhancing the security of data encryption. In addition to enhancing encryption processes, neural networks also contribute to improving anomaly detection, key management and intrusion prevention.

What makes up a neural network?

Neurons are the remarkable foundational units of neural networks. One of the most compelling advantages of neural networks lies in their capacity to learn and derive useful representations of information. This feature sets them apart from traditional rule-based programming, making neural networks an enticing and powerful solution for a wide range of applications.

While a single neuron possesses certain limitations–being capable of solely modeling linear relationships between inputs and outputs–the realm of complex, nonlinear and high-dimensional data patterns demands a more sophisticated approach. This is where the true power and flexibility of neural networks emerge. By amalgamating multiple neurons in interconnected layers, neural networks effectively transcend the constraints of individual neurons, creating a robust framework for capturing intricate patterns and embracing nonlinear relationships. Also, neural networks excel at incorporating contextual information and adeptly handling real-world data.

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