Zafer Attal, M.Sc.

In today’s data-driven world, processing approaches are typically divided between cloud-based solutions—with virtually unlimited resources—and localized processing, which is constrained by hardware limitations. While the cloud offers extensive computational power, localized processing is often required for real-time applications where latency and data security are critical concerns.

To bridge this gap, various algorithms have been developed to pre-process data or extract essential information before it is sent to the cloud.

The goal of this seminar is to explore and compare these algorithms, evaluating their computational load on local hardware and their overall impact on system performance.

About the Project
Modern vehicles rely on complex distributed systems and generate extensive runtime data from ECUs and in-vehicle networks. These data streams must be analyzed effectively to detect sporadic anomalies. The Diagnosis Unit (DU) currently has no integration with the cloud, which limits the possibility of remote configuration and coordination of local DU during runtime. In highly automated vehicles, real-time anomaly diagnosis is essential for safety, reliability, and early intervention. The current Diagnosis Unit (DU) architecture detects anomalies via Ethernet snooping and trace monitoring but lacks embedded intelligence to autonomously categorize anomalies.

Project Description
This thesis aims to bridge that gap by developing and deploying a lightweight Machine Learning classifier capable of locally identifying the type of anomaly based on metadata (e.g., message rates, ID sequences) and trace-level indicators (e.g., control flow deviations, instruction durations, executed functions). The classifier must be tailored for low-power, runtime embedded systems like the ZCU102 board, ensuring it meets latency, memory, and CPU constraints.

The key tasks for this internship include:

• Build an anomaly classification dataset using real and synthetic traces. 
• Design a minimal-overhead classifier suitable for embedded edge platforms. 
• Compare classification techniques (e.g., decision trees, TinyML NNs, rule-based logic). 
• Optimize the model for execution speed and memory footprint. 
• Integrate and validate the classifier within the DU software stack. 
• Quantitatively evaluate accuracy, timing, and resource utilization under realistic conditions

Key Responsibilities: 

•  Dataset Generation: Create labeled datasets using synthetic trace injections and logged anomaly traces from Aurix boards. 
• Model Development: ? Design candidate classifiers using scikit-learn and/or TensorFlow Lite for Microcontrollers. ? Evaluate trade-offs: accuracy vs. latency vs. Footprint.
• Embedded Integration: ? Port the final model to C/C++ for execution on the DU Processing System (Linux). ? Interface classifier with DU anomaly metadata and trace analyzer. 
• Evaluation: ? Test classifier on live or replayed data. ? Measure detection latency, false positives/negatives, inference time, and CPU/RAM usage. 
• Reporting & Documentation: ? Document training pipeline, performance evaluation, and embedded integration. ? Prepare thesis manuscript and possibly a conference/poster paper.

Prerequisites