Potential software weakness, which can lead to exploitable security vulnerabilities, continues to pose a risk to computer systems. According to Common Vulnerability and Exposures, 14,714 vulnerabilities were reported in 2017, more than twice the number reported in 2016. Automated vulnerability detection was recommended to efficiently detect vulnerabilities. Among detection techniques, static binary analysis detects software weakness based on existing patterns. In addition, it is based on existing patterns or rules, making it difficult to add and patch new rules whenever an unknown vulnerability is encountered. To overcome this limitation, we propose a new method—Instruction2vec—an improved static binary analysis technique using machine. Our framework consists of two steps: (1) it models assembly code efficiently using Instruction2vec, based on Word2vec; and (2) it learns the features of software weakness code using the feature extraction of Text-CNN without creating patterns or rules and detects new software weakness. We compared the preprocessing performance of three frameworks—Instruction2vec, Word2vec, and Binary2img—to assess the efficiency of Instruction2vec. We used the Juliet Test Suite, particularly the part related to Common Weakness Enumeration(CWE)-121, for training and Securely Taking On New Executable Software of Uncertain Provenance (STONESOUP) for testing. Experimental results show that the proposed scheme can detect software vulnerabilities with an accuracy of 91% of the assembly code.
The SARS-CoV-2 pandemic has created a global public crisis and heavily affected personal lives, healthcare systems, and global economies. Virus variants are continuously emerging, and, thus, the pandemic has been ongoing for over two years. Vaccines were rapidly developed based on the original SARS-CoV-2 (Wuhan-Hu-1) to build immunity against the coronavirus disease. However, they had a very low effect on the virus’ variants due to their low cross-reactivity. In this study, a multivalent SARS-CoV-2 vaccine was developed using ferritin nanocages, which display the spike protein from the Wuhan-Hu-1, B.1.351, or B.1.429 SARS-CoV-2 on their surfaces. We show that the mixture of three SARS-CoV-2 spike-protein-displaying nanocages elicits CD4+ and CD8+ T cells and B-cell immunity successfully in vivo. Furthermore, they generate a more consistent antibody response against the B.1.351 and B.1.429 variants than a monovalent vaccine. This leads us to believe that the proposed ferritin-nanocage-based multivalent vaccine platform will provide strong protection against emerging SARS-CoV-2 variants of concern (VOCs).
Abstract:In NAND flash systems, the mapping table and related metadata information of FTL should be recovered from sudden power loss to provide data consistency. Nowadays, a lot of FTLs have been reported, however, there are little development reports for FTL recovery schemes. In this paper, we design fast metadata logging and recovery scheme to support efficient and fast power loss recovery. The designed recovery scheme is based on the page-level mapping FTL. In the designed FTL, All the metadata changes are logged into dedicated flash block, which minimize the frequency of metadata updates. At the recovery stage, only metadata blocks are retrieved. The designed FTL metadata logging and recovery scheme support fast sudden power loss recovery, with small amount of metadata logging overhead.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.