RESUMEN
With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.
RESUMEN
To measure plasma density and magnetic fluctuations on the HL-2M tokamak simultaneously, a new diagnostic system combining Doppler backscattering (DBS) and cross-polarization scattering (CPS) is under development. It is essential to understand the propagation of injected and scattered rays to support the electronic/quasi-optical design and subsequent interpretation of the detected signals of the multi-channel DBS/CPS measurements. Thus, ray-tracing analysis with the axisymmetric ray-tracing code BORAY has been performed to estimate the scattering location and wavenumbers of the density and magnetic fluctuations. In addition, the influence of accordance between toroidal and poloidal launch angles is investigated. The received DBS/CPS signal quality can be improved by matching the parallel wavenumber in the direction of magnetic field lines.