Enhancing Automotive Cybersecurity with AI

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Enhancing Automotive Cybersecurity with AI

The fast phased advancement in automotive technology has led to increasingly connected and autonomous vehicles, adding luxury, convenience and efficiency to the users. But at the same time, the connectivity and software driven approach exposing vehicles to the significant cybersecurity risks. Leveraging Artificial Intelligence (AI) to enhance automotive cybersecurity offers a promising solution to these challenges. This opinion article explores the possibilities of AI in enhancing automotive cybersecurity in the upcoming years
How can AI help Automotive Cybersecurity?
AI can help to enhances automotive cybersecurity through its capabilities in anomaly detection, threat prediction, automated penetration testing etc. By analyzing vast data in real-time, AI can identify and try to mitigate cyber threats more effectively than conventional methods.

  1. Anomaly Detection
    Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behaviour. AI can identify such anomaly in vehicle’s behaviour due to changes in its external or in-vehicle network traffic or data pattern through machine learning (ML) or statistical analysis. Machine learning algorithms can be trained to learn the normal operational patterns based on user profiles and operating conditions, enabling them to detect deviations that may indicate a cyberattack. With the statistical probability distribution approaches AI algorithm can decide whether such irregular behaviour is a probability or an attack.
    As an example, in normal case if an Active safety ECU sends CAN data to the Gateway ECU every 100 ms, in case of cyber-attack, it may send more frequent or slow data, may send implausible data, may report multiple failures or may just fuzz the network. In such cases, AI based intrusion detection systems (IDS) can monitor in-vehicle communication networks for these types of abnormal messages or activities and can alert the driver or take predefined measures.
    Tool examples for Anomaly Detection: Darktrace & Vectra AI. Though these tools are not made for Automotive security, they can be tailored for automotive applications and networks.
  2. Risk/Threat predictions
    By having a strong vector database and predictive modelling capabilities, AI can predict the new vulnerabilities and its potential to exploit the ECU SW. The SW might be strong by the time of production but can become week when new vulnerability or zero-day attack happens on the similar system. AI being proactive in predicting it based on the SW information it has in its vector database, can alert the system about it and take some counter actions for the security of user.
    As an example, AI is aware of the secure flashing and CMAC verification during secure boot of the ECU. When a new vulnerability is identified in the HSM that can compromise the key handling mechanism, AI can predict whether such compromise will affect ECU SW or not. If it affects, AI can inform the user about it or can stop the ECU booting itself.
    Tool examples: Recorded Future is one such tool which offers real-time threat intelligence, that can be used to anticipate and mitigate threats. ThreatConnect is another tool which integrates threat intelligence with security operations to provide potential threat factors.
  1. AI powered penetration testing
    Penetration testing of vehicles is the simulated ethical attack on the automotive computer system and its network capabilities. An AI powered penetration testing can perform the test in different way by incorporating all known vulnerability stored in its vector database. The cybersecurity goals of the automotive system and possible threats/risks can be filtered out with the AI powered evaluation metrics. The corresponding test cases can be automated and executed as part of penetration testing. With advanced implementations, even self-execution of these tests periodically should be possible for critical computer systems like autonomous driving.
    Though there are no good examples of AI powered automotive penetration test tools, the conventional pen test tools can be integrated with AI modules to make it a custom solution that will automate and enhance the testing process to effectively identify and mitigate vulnerabilities.

Key AI Technologies that can be used in Automotive Cybersecurity

  1. Machine Learning (ML)
    Machine learning algorithms are essential for analyzing and understanding of vast logs generated by modern vehicles. Supervised & unsupervised techniques can be employed to enhance cybersecurity measures based on such data. Supervised Learning is used for training models on labeled datasets to recognize known threats where as Unsupervised Learning helps in identifying previously unknown threats by discovering hidden patterns in data.
  1. Deep Learning (DL)
    Deep learning, a subset of machine learning, involves neural networks with many layers that can model complex patterns in data. It is particularly effective in image and speech recognition, which can be applied to enhance driver authentication and vehicle surveillance systems. This method can detect complex, sophisticated attacks and thefts that traditional methods might miss.
  1. Natural Language Processing (NLP)
    NLP allows vehicles to understand large volumes of textual data from cyber threat reports, forums, and dark web to gather actionable intelligence. Even it can be used to recognize voice commands, enhancing both user experience and security.

Challenges of implementing AI in automotive system

While AI offers significant benefits for automotive cybersecurity, its implementation has several challenges in an road vehicle ecosystem.

Complexity: Since Automotive system has several interconnected ECUs, intelligent sensors and actuators, its difficult to integrate AI solutions seamlessly. Also since there are several hundreds of suppliers with wide range of hardware and software solutions, it’s not a uniform solution that can be provided to all the road vehicles.

Data Privacy: Ensuring the privacy of data collected by AI systems is crucial. Since some of the vehicle data is highly sensitive (e.g., location, driver behavior), measures must be taken to anonymize data and comply with regulations such as GDPR.

Model Training: AI models require vast amounts of data for training. Collaboration among manufacturers, suppliers, and cybersecurity firms is needed in aggregating sufficient data while maintaining confidentiality.

Computational Resources: AI systems require significant computational power. Efficient resource-constrained environments like vehicles. Scalability and maintenance across different vehicle brands and models to support the threat prevention is a huge resource demanding solution.

Continuous Learning: Cyber threats constantly evolve, necessitating continuous learning and updating of AI models. Implementing mechanisms for ongoing model training and adaptation is essential.

Functional safety: AI systems in vehicles must meet stringent safety standards and certifications like ISO26262 which can be complex and time-consuming to achieve. The freedom from interference and redundancy needs of functional safety will add up additional resource and computational power making AI implementations hard.

Conclusion

AI holds immense potential to revolutionize automotive cybersecurity by providing advanced, proactive, and automated defense mechanisms against cyber risks and threats. By leveraging AI technologies such as machine learning, deep learning, and natural language processing, the automotive industry can significantly enhance the security of modern vehicles. However, careful considerations for over complexity, data privacy, computational resources, continuous learning & safety is essential for effective implementation.

Siri AB is Gothenburg, Sweden based organisation with expertise in Automotive, Telecom and IoT engineering. Siri AB is expanding its capabilities towards AI and planning to enable AI powered cybersecurity for Automotive use cases.

Ishwara prasada S

About the Author

Ishwaraprasada is the Head of Cyber Security Services in Siri AB with several years of experience in the Automotive domain. His area of expertise includes Cyber security, Functional safety, Autosar, Base Software, Battery management system, Vehicle Charging standards, Inverters & Engine management.

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