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Neural Networks Can Help Keep Connected Vehicles Secure

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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.

Schematic showing synthesis of neural networks.
Figure 1: Schematic showing synthesis of neural networks

Figure 1 illustrates a single hidden-layer neural network, known as a shallow neural network. This type of architecture comprises a single hidden layer of neurons, alongside the input and output layers. It represents the most straightforward form of a neural network, characterized by its limited number of layers. Shallow neural networks are designed to be simpler and computationally less demanding. They are well-suited for addressing uncomplicated problems that do not need high complexity or hierarchical representation.

In a single hidden-layer neural network, the input layer receives the input data, while the output layer generates the final output. The single hidden layer positioned between the input and output layers performs computations on the input data before transmitting it to the output layer. However, unlike deep neural networks, shallow neural networks lack additional hidden layers beyond the initial one.

Neural network FPGA implementation and simulation

Neurons in hidden layer.
Figure 2: Neurons in hidden layer

In the realm of neural networks, the exclusive OR (XOR) operation holds a significant place as a benchmark problem, owing to its inherent nonlinearity. Although a single neuron cannot directly model XOR, the power of neural networks truly shines when multiple neurons come together to tackle XOR-like operations. Consider Figure 2, which visually represents a nonlinear binary classification of the XOR problem. It demonstrates the remarkable capability of neural networks to learn intricate decision boundaries that transcend simple linear relationships.

This exemplifies the ability of neural networks to capture complex patterns and extract meaningful features from the input data. Through an iterative learning process, neural networks adeptly discover the underlying structure of the XOR problem, enabling them to make precise classifications and navigate nonlinear decision landscapes. Figure 2 serves as a visual testament to the power of neural networks, showcasing their ability to conquer XOR-like problems and highlighting the importance of appropriate architecture and training techniques to achieve accurate and effective results.

Simulation result of neural network.
Figure 3: Simulation result of neural network

Figure 3 presents a representation of the field-programmable gate array (FPGA) simulation output for the XOR operation of neural network implementation in the FPGA shown in Figure 2, providing insight into its behavior across different input combinations. As a fundamental logical operation, XOR exhibits a distinct characteristic: It generates a result of 1 (true) when the inputs differ, specifically when one input is 0 and the other is 1, or vice versa. Conversely, when both inputs are the same (either both 0 or both 1), the XOR operation yields a result of 0 (false).

By observing Figure 3, viewers can clearly understand how the XOR operation is implemented within a neural network. It showcases the network’s ability to discern and respond to variations in input values, accurately reflecting the desired XOR behavior. The visualization effectively captures the essence of the XOR operation’s functionality, emphasizing its capability to differentiate between contrasting input combinations while consistently delivering predictable outputs.

Simulations, such as the one presented in Figure 3, play a vital role in comprehending and validating the behavior of logical operations within neural networks. They serve as invaluable tools for verifying the correct implementation of XOR and other logical operations in diverse applications, including digital circuit design, communication protocols and cryptographic algorithms. These simulations aid in ensuring the reliability, performance and accuracy of neural-network–based solutions in a wide range of domains.

XOR data.
Table 1: XOR data

In Table 1, the XOR operation, with its distinctive property of producing true only when the true inputs are odd numbers, can be applied to significant applications in automotive and cybersecurity domains. It plays a pivotal role in fault detection, cyclic redundancy check in data transmission, sensor fusion for inconsistency identification, encryption for secure data communication, cryptographic hashing for data integrity and scrambling techniques for data obfuscation. The XOR operation’s versatility and widespread adoption highlight its fundamental importance in ensuring reliability, security and integrity in these domains.

Using the power of neural networks to protect data privacy

By leveraging the power of neural networks, vehicles can benefit from stronger data-encryption mechanisms and heightened security measures to protect against potential data breaches and unauthorized access.

Overall, neural networks, with their layered architectures and interconnected neurons, offer a powerful and versatile framework for addressing complex problems and extracting insights from large-scale and high-dimensional data.

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