This article offers a novel perspective on identifying the critical branches under load redistribution (LR) attacks. Compared to the existing literature that is largely disruption-driven and based on dc state estimation, we propose to address the threat from LR attacks on a more fundamental level by modeling and analyzing the circulation of false data within the cyber network resulting from the coordinated branch and node measurement manipulation based on ac state estimation. We reveal the underlying mechanism that disturbing the coordinated and reconciled interactions among false data injections can effectively sever the completeness and consistency of the LR attack, thus reducing its damaging effect. We then develop a scalable and computationally efficient critical branch identification approach that evaluates and ranks branches in terms of their criticality according to the graph model of the false data circulation. Case studies are conducted on IEEE 14-, 39-, 118-bus systems and several large-scale models to validate the effectiveness and computational efficiency of the proposed approach. Simulation results show that the proposed approach scales well with the size of the system and can effectively mitigate the damaging effects of the LR attack in terms of operation cost and load shedding.

The Image Captioning (IC) technique is widely used to describe images in natural language. However, even state-of-the-art IC systems can still produce incorrect captions and lead to misunderstandings. Recently, some IC system testing methods have been proposed. However, these methods still rely on pre-annotated information and hence cannot really alleviate the difficulty in identifying the test oracle. Furthermore, their methods artificially manipulate objects, which may generate unreal images as test cases and thus lead to less meaningful testing results. Thirdly, existing methods have various requirements on the eligibility of source test cases, and hence cannot fully utilize the given images to perform testing. To tackle these issues, in this paper, we proposeReICto perform metamorphic testing for the IC systems with some image-level reduction transformations like image cropping and stretching. Instead of relying on the pre-annotated information,ReICuses a localization method to align objects in the caption with corresponding objects in the image, and checks whether each object is correctly described or deleted in the caption after transformation. With the image-level reduction transformations,ReICdoes not artificially manipulate any objects and hence can avoid generating unreal follow-up images. Additionally, it eliminates the requirement on the eligibility of source test cases during the metamorphic transformation process, as well as decreases the ambiguity and boosts the diversity among the follow-up test cases, which consequently enables testing to be performed on any test image and reveals more distinct valid violations. We employReICto test five popular IC systems. The results demonstrate thatReICcan sufficiently leverage the provided test images to generate follow-up cases of good realism, and effectively detect a great number of distinct violations, without the need for any pre-annotated information.