GraphMoco:a Graph Momentum Contrast Model that Using Multimodel Structure Information for Large-scale Binary Function Representation Learning

In the field of cybersecurity, the ability to compute similarity scores at the function level is import. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining on erroneous labels. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale BCSD. To overcome the aforementioned problems, we propose GraphMoco: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. Our approach employs a CNN-based model and departs from the usage of memory-intensive pre-trained models. We adopt an unsupervised learning strategy that effectively use the intrinsic structural information present in the binary code. Our approach eliminates the need for manual labeling of similar or dissimilar information.Importantly, GraphMoco demonstrates exceptional performance in terms of both efficiency and accuracy when operating on extensive datasets. Our experimental results indicate that our method surpasses the current SOTA approaches in terms of accuracy.Comment: 22 pages,7 figure

Selective sphingosine-1-phosphate receptor 1 modulator attenuates blood–brain barrier disruption following traumatic brain injury by inhibiting vesicular transcytosis

Abstract Background Traumatic brain injury (TBI) provokes secondary pathological damage, such as damage to the blood–brain barrier (BBB), ischaemia and inflammation. Major facilitator superfamily domain-containing 2a (Mfsd2a) has been demonstrated to be critical in limiting the increase in BBB vesicle transcytosis following brain injury. Recent studies suggest that a novel and selective modulator of the sphingosine-1-phosphate receptor 1 (S1P1), CYM-5442, maintains the integrity of the BBB by restricting vesicle transcytosis during acute ischaemic stroke. In the current study, we investigated whether CYM-5442, evaluated in a short-term study, could protect the brains of mice with acute-stage TBI by reversing the increase in vesicle transport due to reduced Mfsd2a expression after TBI. Methods We used the well-characterized model of TBI caused by controlled cortical impact. CYM-5442 (0.3, 1, 3 mg/kg) was intraperitoneally injected 30 min after surgery for 7 consecutive days. To investigate the effect of CYM-5442 on vesicle transcytosis, we downregulated and upregulated Mfsd2a expression using a specific AAV prior to evaluation of the TBI model. MRI scanning, cerebral blood flow, circulating blood counts, ELISA, TEM, WB, and immunostaining evaluations were performed after brain injury. Results CYM-5442 significantly attenuated neurological deficits and reduced brain oedema in TBI mice. CYM-5442 transiently suppressed lymphocyte trafficking but did not induce persistent lymphocytopenia. After TBI, the levels of Mfsd2a were decreased significantly, while the levels of CAV-1 and albumin were increased. In addition, Mfsd2a deficiency caused inadequate sphingosine-1-phosphate (S1P) transport in the brain parenchyma, and the regulation of BBB permeability by Mfsd2a after TBI was shown to be related to changes in vesicle transcytosis. Downregulation of Mfsd2a in mice markedly increased the BBB permeability, neurological deficit scores, and brain water contents after TBI. Intervention with CYM-5442 after TBI protected the BBB by significantly reducing the vesicle transcytosis of cerebrovascular endothelial cells. Conclusion In addition to transiently suppressing lymphocytes, CYM-5442 alleviated the neurological deficits, cerebral edema and protective BBB permeability in TBI mice by reducing the vesicle transcytosis of cerebrovascular endothelial cells