Master's thesis: Interpretable CAN Bus Intrusion Forensics via Tensor Networks
Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science · Cybersecurity
In short
Responsibilities
- Conduct a literature review on automotive cybersecurity, CAN bus security, intrusion detection systems, tensor networks, and quantum-inspired machine learning.
- Study common CAN attack scenarios, including DoS, spoofing, fuzzing, replay, and masquerading attacks.
- Design suitable feature representations for CAN traffic, incorporating arbitration IDs, payload content, message sequences, and timing information.
- Develop and implement a tensor network based anomaly detection framework.
- Investigate interpretability for attributing anomalies to specific CAN messages and communication patterns.
- Evaluate the proposed method using public automotive cybersecurity datasets and compare results with conventional machine learning and deep learning approaches.
- Analyze model compactness, reproducibility, detection performance, and suitability for forensic investigations.
- Document findings and provide recommendations for future deployment of interpretable AI methods in automotive cybersecurity.
Requirements
- Motivated master’s student
- Basic knowledge in computer science and cybersecurity
- Specific skills in machine learning
- Proficiency in programming languages such as Python
- Proficiency in machine learning frameworks
Desired Qualifications
- Experience with tensor networks and quantum-inspired machine learning is a plus.
Benefits
- Collaboration with Swedish industrial and academic partners
- Opportunity to work on cutting-edge security solutions
- Flexibility to work partly remotely
- Close collaboration with the Swedish automotive industry
Skills
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Job Posted
4 days ago
Expires
in 2 weeks
Employment Type
Internship
Work mode
Hybrid
Experience Level
Entry
Locations
Kista, Sweden
Sundsvall, Sweden
Qualification
Applicants
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