Ai Enabled EV Intrusion Detection System
Background/Problem Statement:
Currently, Electric vehicles (EVs) have gained significant demand due to their eco-friendly and cost-effective nature. However, with the increasing connectivity and complexity of EV systems, the risk of cyberattacks targeting these vehicles has also vastly increased. So, ensuring the security of EVs is crucial to prevent malicious intrusions that could compromise vehicle safety, privacy, and functionality. The aim of this project is to develop an intrusion detection system using our Ai proprietary engine MinskyTM which is capable of accurately classifying different types of cyber-attacks on EV systems, thereby enhancing the overall security of these vehicles
Solution Overview:
After thorough evaluation of data, we proposed a solution for the intrusion detection problem in electric vehicles (EVs) by using our Ai proprietary engine MinskyTM to accurately build a predictive model for a real-time monitoring of Network data that uses machine learning and anomaly detection techniques. These models used historical dataset comprised of attributes collected from the Network Management Systems such as flow duration, total forward packets, Total backward packets, Total Length of Fwd. Packets, Fwd. Packet Length Max, Fwd. Packet Length Min etc. to classify various types of attacks on EV systems. Data from sensors and communication modules within the EV is collected and submitted to the predictive models to identify anomalies, both known and unknown. To ensure privacy, privacy-preserving measures are implemented, and regular updates are carried out to adapt to evolving attack methods. Collaboration with EV manufacturers is essential to seamlessly integrate intrusion detection into the vehicle's security architecture. This comprehensive approach aims to enhance the security and privacy of EV systems, safeguarding the safety and functionality of these vehicles.
Challenges
- Diverse Attack Types: Attacks on EVs can vary widely, including software exploits, hardware tampering and wireless communication breaches. Detecting and classifying these diverse attack types in Real-Time is a significant challenge.
- Real-time Monitoring: EVs require real-time monitoring to detect and respond to threats promptly. Delays in detecting and responding to attacks could lead to severe consequences, including accidents and data breaches.
- Limited Computing Resources: Many EVs have limited computing resources, making it challenging to implement resource-intensive intrusion detection systems.
- Data Privacy Concerns: The data collected for intrusion detection must respect user privacy, making it challenging to balance security with privacy concerns.
Key Benefits (Minsky):
- User-Friendly cloud-based AI platform
- Scalable across various domains/data.
- Provides you a list of % dependency features that can be used to optimize the outcomes.
- Ability to fine tune or optimize the models by trying different algorithms / prediction attributes
- Easy integration with other third-party solutions such as TABLEAU for data visualization
Results
- Improved Security: Enhanced protection against various types of attacks.
- Real-time Threat Response: Since the Ai Models stored in the Vehicle, this leads to Prompt detection and response to potential threats.
- Data Privacy Protection: Privacy-preserving measures are implemented to protect sensitive information.
- Adaptability: Regular model updates to address evolving attack methods.
- Enhanced Collaboration: Closer integration with EV manufacturers for comprehensive security.
