Linux kernels are being widely deployed in embedded applications, such as increasingly automated vehicles and robots, due to their robust ecosystem. Security modules have been developed to enhance the integrity of Linux kernels, a critical system component. However, these modules consume substantial computational resources, making them unsuitable for embedded domains. We introduce LightRIM, a lightweight method to measure the Linux kernel’s integrity during runtime, ideal for resource-limited embedded applications. We focus on major attack types and extract objects for monitoring. Our approach includes a two-stage hashing process and an event-triggered measurement algorithm tied to the security value. To mitigate Time-of-Check-to-Time-of-Use (TOCTOU) attacks, we introduce a heuristic algorithm that maximizes the attack detection rate within CPU usage constraint and randomizes the measurement intervals. Experimental results indicate that LightRIM incurs less than 0.7% performance overhead while providing extensive attack coverage.

This paper aims at generating anomalous images and their segmentation labels to address the lack of real-world anomaly samples and privacy issues. Departing from conventional approaches that use masks solely to guide the generation of anomaly images, we propose a dual-branch training strategy for the generative model. This strategy enables the simultaneous production of anomaly images and masks, with an alignment regularization loss that ensures the coherence between the generated images and their masks. During inference, only the image-generation branch is activated to produce synthetic samples for training the downstream segmentation model. Furthermore, we propose to integrate the well-trained generative model into the training of segmentation models, utilizing a generative feedback loss to refine the segmentation model's performance. Experiments show our method's IoU metrics exceed previous methods by 5.03%, 5.68% and 16.63% on Real-IAD (industrial), polyp (medical), and Floor Dirty (indoor) datasets. The code is publicly accessible at https://github.com/huan-yin/anomaly-alignment.