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Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise andmore »leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub.« less

Basic helix–loop–helix (bHLH) transcription factors constitute a superfamily in eukaryotes, but their roles in plant immunity remain largely uncharacterized. We found that the transcript abundance in tomato (Solanum lycopersicum) leaves of one bHLH transcription factor-encoding gene, negative regulator of resistance to DC3000 1 (Nrd1), increased significantly after treatment with the immunity-inducing flgII-28 peptide. Plants carrying a loss-of-function mutation in Nrd1 (Δnrd1) showed enhanced resistance to Pseudomonas syringae pv. tomato (Pst) DC3000 although early pattern-triggered immunity responses, such as generation of reactive oxygen species and activation of mitogen-activated protein kinases after treatment with flagellin-derived flg22 and flgII-28 peptides, were unaltered compared to wild-type plants. RNA-sequencing (RNA-seq) analysis identified a gene, Arabinogalactan protein 1 (Agp1), whose expression is strongly suppressed in an Nrd1-dependent manner. Agp1 encodes an arabinogalactan protein, and overexpression of the Agp1 gene in Nicotiana benthamiana led to ∼10-fold less Pst growth compared to the control. These results suggest that the Nrd1 protein promotes tomato susceptibility to Pst by suppressing the defense gene Agp1. RNA-seq also revealed that the loss of Nrd1 function has no effect on the transcript abundance of immunity-associated genes, including AvrPtoB tomato-interacting 9 (Bti9), Cold-shock protein receptor (Core), Flagellin sensing 2 (Fls2), Flagellin sensing (Fls3),more »and Wall-associated kinase 1 (Wak1) upon Pst inoculation, suggesting that the enhanced immunity observed in the Δnrd1 mutants is due to the activation of key PRR signaling components as well as the loss of Nrd1-regulated suppression of Agp1.

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All living cells generate structurally complex and compositionally diverse spectra of glycans and glycoconjugates, critical for organismal evolution, development, functioning, defense, and survival. Glycosyltransferases (GTs) catalyze the glycosylation reaction between activated sugar and acceptor substrate to synthesize a wide variety of glycans. GTs are distributed among more than 130 gene families and are involved in metabolic processes, signal pathways, cell wall polysaccharide biosynthesis, cell development, and growth. Glycosylation mainly takes place in the endoplasmic reticulum (ER) and Golgi, where GTs and glycosidases involved in this process are distributed to different locations of these compartments and sequentially add or cleave various sugars to synthesize the final products of glycosylation. Therefore, delivery of these enzymes to the proper locations, the glycosylation sites, in the cell is essential and involves numerous secretory pathway components. This review presents the current state of knowledge about the mechanisms of protein trafficking between ER and Golgi. It describes what is known about the primary components of protein sorting machinery and trafficking, which are recognition sites on the proteins that are important for their interaction with the critical components of this machinery.