With the rapid proliferation of the Internet of Things (IoT), the security of IoT devices has become an increasingly critical concern, particularly in the context of lightweight cryptographic algorithms designed for resource-constrained environments. Lightweight cryptography is indispensable for ensuring the confidentiality and integrity of communications across IoT networks. This paper presents a thorough security evaluation of the Sycon algorithm, focusing on its susceptibility to a range of cryptographic attacks. We begin by introducing Meet-in-the-Middle (MitM) preimage and collision attacks on 3-round and 4-round Sycon-Hash. These attacks are the first published analysis results on reduced-round Sycon-Hash. By employing an SMT-based modeling approach in conjunction with the STP solver, we successfully construct and validate MitM attack paths, thereby providing novel insights into the resilience of Sycon against such threats. In addition, we propose and implement a committing attack on the 2-round Sycon-AEAD-64 using the CMT-3 framework, which uncovers potential vulnerabilities within its authenticated encryption mechanism. Our analysis not only advances the understanding of Sycon’s cryptographic robustness but also offers valuable methodologies for the future assessment of lightweight cryptographic solutions in resource-constrained contexts.

Personalized micro video recommendation aims to recommend the micro videos tailored to user preference based on the user’s interaction history with the micro videos, which has drawn increasing attention from both the academic and industrial communities. Existing solutions primarily concentrate on video-level interactions between users and micro videos to model their preferences, and cannot distinguish the finer-grained users’ interactions with various modalities. Ignoring modality-level interactions prevents the full understanding of the user’s true and subtle preferences on micro videos. To this end, in this article, we propose a Contrastive Multimodal Interaction Graph Learning ( MVideoRec ) model to automatically and explicitly learn the modality-level interaction between users and micro videos for recommendations. Specifically, we designed a graph structure learning module with a sparsification strategy to infer modality-level interaction graph, which will be dynamically and iteratively updated based on the node representations obtained from the node representation learning module. Furthermore, to address the lack of ground truth labels, we propose to generate teacher view from video-level interaction graph and student view from modality-level interaction graph, as well as construct intra-modality and inter-modality contrastive pairwise instances to provide self-supervised signals. Extensive experiments on three real-world micro video datasets validate the effectiveness of MVideoRec.