In the context of rapid advancements in intelligent transportation and autonomous driving technologies, Anomaly Detection in Video/Sequential Images has emerged as a core technology for ensuring driving safety and enhancing system robustness. Unlike traditional static image recognition, vehicles operate in a highly dynamic, complex, and unpredictable three-dimensional world. Therefore, the ability to process continuous visual information in real-time and identify "unusual" events is crucial for intelligent vehicles.

Here, we aim to investigate which algorithms are best suited to the application and what advantages and disadvantages they offer. 

As automotive architectures transition from legacy bus systems to high-speed Automotive Ethernet, the Audio Video Transport Protocol (AVTP) has become the standard for transporting time-sensitive data, including ADAS camera feeds, infotainment streams, and critical control traffic. However, this transition opens new attack vectors: an adversary could inject malicious frames to freeze a backup camera or spoof control messages within the AVTP stream, potentially leading to catastrophic failures.

This seminar focuses on the unique challenges and methodologies for implementing Intrusion Detection Systems (IDS) within the IEEE 1722 framework. 

As automotive architectures transition from legacy bus systems to high-speed Automotive Ethernet, the Audio Video Transport Protocol (AVTP) has become the standard for transporting time-sensitive data, including ADAS camera feeds, infotainment streams, and critical control traffic. However, this transition opens new attack vectors: malicious frames can be injected to freeze the camera or spoof control messages within the AVTP stream, potentially leading to catastrophic failures.

The current detection methods exploit the consistency of the Ethernet Frame header. They can detect malicious packets with abnormal sequence numbers in their headers. However, if the anomalies happen before the Ethernet transaction (on the broadcaster or in the real world) or the intrusion is well designed to have a normal header, the current detection systems are at a loss.

To address this scenario, we want to develop an unsupervised Anomaly Detection System to detect abnormal behavior in AVTP packets. More specifically, we want to detect deviations across several consecutive image frames to assess the normality of each image at the packet level. The idea is that the Ethernet packets are supposed to contain the data in the same manner. For example, the first packet should always contain the first chunk of the Intra Frames. So the image to compare the content is equivalent to comparing a small part of the image.

Our research targets three different circumstances:

• An anomaly in the real world, e.g., a damaged camera or obstacles in front of the camera.
• Intrusion on the broadcast, memory replacement, leading to a sequence of AVTP frames with normal headers.

• Injection during transmission. The malicious attacker sends replayed frames with abnormal headers.

Since the AVTP protocol is commonly used to transmit video footage, we can transfer the concept of Abrupt Detection of Image into our system.

The work can be separated into the following parts.

• Literature review for image cut detection (feature extraction)
• Implement a lightweight, unsupervised anomaly detection system for the image sequence.
• Transfer it to the AVTP Ethernet Frames
• If possible, expand the model to the general Ethernet protocols.

Voraussetzungen

Hyperdimensional Computing is drawing attention as a novel brain-inspired computing paradigm. By exploiting the ultra-high-dimensional random vector, it can deliver comparable accuracy while consuming low energy. Therefore, this approach is ideal for energy-limited scenarios in which cognitive tasks are performed. Due to the algorithm's high parallelism (as in vector computation), it is commonly implemented on a proprietary hardware accelerator.

However, the raw HDC algorithm cannot achieve the same level of accuracy as DNN in many tasks, such as MNIST or Face Recognition. This is considered a major disadvantage of HDC. Therefore, many training techniques have been developed to improve the accuracy of HDC models, such as online and adaptive learning. While these techniques can significantly improve the model's performance, they use real numbers or multi-bit precision during training. This is deviating from a hardware-friendly algorithm. Besides, they typically need to retrain the model to achieve higher accuracy, which can be burdensome when training on computation-limited devices.

Here, we focus on a under-developed technique: multi-centroid. By introducing multiple centroids in the model. It can resolve the data imbalance and low memory parallelism. This technique is encoding-irrelevant and can be employed in basically all the feature-based classification tasks.

The student is supposed to implement a model and explore its benefits and drawbacks across different tasks, such as MNIST, Hand Gesture, and ISOLET. 

Voraussetzungen