Mutation-based greybox fuzzing is a highly effective and widely used technique to find bugs in software. Provided initial seeds, fuzzers continuously generate test cases to test the software by mutating a seed input. However, the majority of them are ''invalid'' because the mutation may destroy the format of the seeds. In this paper, we present a knowledge-learn evolutionary fuzzer based on AFL, which is called LearnAFL. LearnAFL does not require any prior knowledge of the application or input format. Based on our format generation theory, LearnAFL can learn partial format knowledge of some paths by analyzing the test cases that exercise the paths. Then LearnAFL uses these format information to mutate the seeds, which is efficient to explore deeper paths and reduce the test cases exercising high-frequency paths than AFL. We compared LearnAFL with AFL and some other state-of-the-art fuzzers on ten real-world programs. The result showed that LearnAFL could reach branch coverage 120% and 110% of that of AFL and FairFuzz, respectively. LearnAFL also found 8 unknown vulnerabilities in GNU Binutils, Libpng and Gif2png, all of which have been reported to the vendors. Besides, we compared the format information learned from the initial seed of an ELF file with a format standard of ELF files. The result showed that LearnAFL learns about 64% part of the file format without any prior knowledge.INDEX TERMS Input format learning, deep path fuzzing, greybox fuzzing, vulnerability detection.
In this paper, a new method of biometric characterization of heart sounds based on multimodal multiscale dispersion entropy is proposed. Firstly, the heart sound is periodically segmented, and then each single-cycle heart sound is decomposed into a group of intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). These IMFs are then segmented to a series of frames, which is used to calculate the refine composite multiscale dispersion entropy (RCMDE) as the characteristic representation of heart sound. In the simulation experiments I, carried out on the open heart sounds database Michigan, Washington and Littman, the feature representation method was combined with the heart sound segmentation method based on logistic regression (LR) and hidden semi-Markov models (HSMM), and feature selection was performed through the Fisher ratio (FR). Finally, the Euclidean distance (ED) and the close principle are used for matching and identification, and the recognition accuracy rate was 96.08%. To improve the practical application value of this method, the proposed method was applied to 80 heart sounds database constructed by 40 volunteer heart sounds to discuss the effect of single-cycle heart sounds with different starting positions on performance in experiment II. The experimental results show that the single-cycle heart sound with the starting position of the start of the first heart sound (S1) has the highest recognition rate of 97.5%. In summary, the proposed method is effective for heart sound biometric recognition.
C. perfringens type C can induce enteritis accompanied by diarrhea and annually causes significant economic losses to the global pig industry. The pathogenic mechanisms of C. perfringens type C in pigs are still largely unknown. To investigate this, we challenged seven-day-old piglets with C. perfringens type C to cause diarrhea. We performed hematoxylin & eosin (H&E) staining of the small intestine (including duodenum, jejunum, and ileum) and assessed gene expression in the ileal tissue. H&E staining of the duodenum, jejunum, and ileum demonstrated inflammation and edema of the lamina propria and submucosa. A total of 2181 differentially expressed genes (DEGs) were obtained in ileal tissues. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis of DEGs indicated that the main pathways were enriched in the T cell receptor signaling pathway, NF-kappa B signaling pathway, and (tumor necrosis factor) TNF signaling pathway. These results provide insights into the pathogenicity of C. perfringens type C and improve our understanding of host–bacteria interactions.
The threadfin sea catfish (Arius arius) belongs to the genus Arius in Ariidae. In this paper, we initially determined the complete mitochondrial genome of Arius arius. The mitochondrial genome is 16, 711 bp in length, with the base composition on the heavy strand: A -29.67%, T -25.38%, C -29.70% and G -15.25%. It has the typical vertebrate mitochondrial gene arrangement, including 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and a control region. Phylogenetic analysis showed that A. arius was clustered into the order of Siluriformes, and closely related to species in the family of Siluridae. The present study would contribute to genetic resources conservation and systematics study of A. arius.
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