Binary code similarity detection (BCSD) is a task of detecting similarity of binary functions which are not available to the corresponding source code. It has been widely utilized to facilitate various kinds of crucial security analysis in software engineering. Because of the complexity of the program compilation process, identifying binary code similarity presents tough challenges. The most sensible binary similarity detector relies on a robust vector representation of binary code. However, few BCSD approaches are suitable to form vector representations for analyzing similarities between binaries, which may not only diverge in semantics but also in structures. And the existing solutions which only depend on hands-on feature engineering to form feature vectors, fail to take into consideration the relationships between instructions. To resolve these problems, we propose a novel and unified approach called DeepDual-SD that aims to combine the dual attributes (semantic and structural attribute). More specifically, DeepDual-SD consists of two branches, in which one text-based feature representation is driven by semantic attribute learning to exploit instruction semantics, another graph-based feature representation for structural attribute learning to investigate structural differences. Meanwhile deep embedding (DE) technology is utilized to map this information into low-dimensional vector representation. In addition, to get together the dual attributes, a fusion mechanism based on gate architecture is designed for learning to pay proper attention between the two attribute-aware embeddings. Experimental verifications are conducted on Openssl and Debian datasets for several tasks, including cross-compiler, cross-architecture and cross-version scenarios. The results demonstrate that our method outperforms the state-of-the-art BCSD methods in different scenarios in terms of detection accuracy.
While playing an increasing role in the field of air-space integrated networks, terminal entities are exposed to more serious security risks than ordinary terminal entities on the ground, including but not limited to astronomical risks (e.g., solar activity), link disruptions, and malicious attacks. In the integrated air-space-space network, after the terminal entity is loaded into the rocket for launch, real-time monitoring measures are implemented on the ground site for all links to the intended orbit. On top of the real-time monitoring measures, the need for a comprehensive evaluation of the air-space integration network is growing. In this paper, we further classify the security requirements and evaluation benchmarks of the terminal entities of the air-space integration network around the security evaluation requirements of the air-space integration network. On this basis, the relevant concept of trust value is introduced to assess the security status of terminal entities of air-space-sky integrated networks, and on this basis, a security assessment system of air-space-sky integrated network terminals is proposed. The exploration and development of field applications are initially realized.
With the rapid growth of information retrieval technology, Chinese text classification, which is the basis of information content security, has become a widely discussed topic. In view of the huge difference compared with English, Chinese text task is more complex in semantic information representations. However, most existing Chinese text classification approaches typically regard feature representation and feature selection as the key points, but fail to take into account the learning strategy that adapts to the task. Besides, these approaches compress the Chinese word into a representation vector, without considering the distribution of the term among the categories of interest. In order to improve the effect of Chinese text classification, a unified method, called Supervised Contrastive Learning with Term Weighting (SCL-TW), is proposed in this paper. Supervised contrastive learning makes full use of a large amount of unlabeled data to improve model stability. In SCL-TW, we calculate the score of term weighting to optimize the process of data augmentation of Chinese text. Subsequently, the transformed features are fed into a temporal convolution network to conduct feature representation. Experimental verifications are conducted on two Chinese benchmark datasets. The results demonstrate that SCL-TW outperforms other advanced Chinese text classification approaches by an amazing margin.
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