Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
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The small non-coding RNA response to virus infection in the Leishmania vector Lutzomyia longipalpis. by Flávia Viana Ferreira, Eric Roberto Guimarães Rocha Aguiar, Roenick Proveti Olmo, Karla Pollyanna Vieira de Oliveira, Emanuele Guimarães Silva, Maurício Roberto Viana Sant'Anna, Nelder de Figueiredo Gontijo, Erna Geessien Kroon, Jean Luc Imler, João Trindade Marques

“…RNA interference (RNAi) mechanisms utilize small non-coding RNAs to regulate different aspects of host-pathogen interactions. …”
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Characterization of Sus scrofa small non-coding RNAs present in both female and male gonads. by Dorota Kowalczykiewicz, Aleksandra Świercz, Luiza Handschuh, Katarzyna Leśniak, Marek Figlerowicz, Jan Wrzesinski

“…Small non-coding RNAs (sncRNAs) are indispensable for proper germ cell development, emphasizing the need for greater elucidation of the mechanisms of germline development and regulation of this process by sncRNAs. …”
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A dataset of hidden small non-coding RNA in the testis of heat-stressed models revealed by Pandora-seq by Mailin Gan, Yuhang Lei, Kai Wang, Yan Wang, Tianci Liao, Jianfeng Ma, Li Zhu, Linyuan Shen

“…In this study, we utilized Pandora-seq technology to detect the small non-coding RNAs (sncRNAs) expression profile in the testicular tissue of heat-stressed mice. …”
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Optimal Design of Linear Space Code for MIMO Optical Wireless Communications by Yan-Yu Zhang, Hong-Yi Yu, Jian-Kang Zhang, Yi-Jun Zhu

“…In this paper, the design of linear full-diversity space code (FDSC) is considered for an intensity-modulated direct-detection multiple-input–multiple-output optical wireless communication (IM/DD MIMO-OWC) system, in which the channel suffers from log-normal fading and different links have different variances. …”
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Small target detection in coal mine underground based on improved RTDETR algorithm by Feng Tian, Cong Song, Xiaopei Liu

“…In order to increase the accuracy of tiny object detection and concentrate on the detail information in the shallow feature map, the small object detection layer P2 is simultaneously added to the Head of the coding section. …”
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DiallelX: a modern fortran code for calculating network cross-correlation by Shiro Hirano, Makoto Naoi

“…Abstract In this paper, we develop a modern Fortran code to approximate network cross-correlation coefficients for seismic event detection. …”
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Two approaches to multiuser detection over fading channels by V. P. Tuzlukov

“…In this paper, two different receiver structures to multiuser detection that are appropriate for the code-division multiple-access systems with antenna arrays in fading channels are investigated and compared. …”
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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Additionally, it incorporates a lightweight detection head that is optimized for small objects, thereby significantly improving detection performance in cluttered and intricate backgrounds. …”
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Welding defect detection with image processing on a custom small dataset: A comparative study by József Szőlősi, Béla J. Szekeres, Péter Magyar, Bán Adrián, Gábor Farkas, Mátyás Andó

“…It lays the groundwork for future exploration in larger datasets and varied welding scenarios, potentially contributing to defect detection practices in manufacturing industries. The dataset and the code repository links are also provided to support our findings.…”
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Small sample smart contract vulnerability detection method based on multi-layer feature fusion by Jinlin Fan, Yaqiong He, Huaiguang Wu

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CSW-YOLO: A traffic sign small target detection algorithm based on YOLOv8. by Qian Shen, Yi Li, YuXiang Zhang, Lei Zhang, ShiHao Liu, Jinhua Wu

“…The model achieves precision on par with existing mainstream algorithms, while being simpler, significantly reducing computational requirements, and being more suitable for small target detection tasks. The source code and test results of the models used in this study are available at https://github.com/lyzzzzyy/CSW-YOLO.git.…”
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X-SPIDE: An eXplainable Machine Learning Pipeline for Detecting Smart Ponzi Contracts in Ethereum by Luca Pennella, Fabio Pinelli, Letterio Galletta

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CSSDet: small object detection via cross-scale feature enhancement on drone-view images by Gui Cheng, Qing Ding, Bowen Cai, Chaoya Dang, Yu Wang, Xiaolong Zuo, Zhenfeng Shao

“…Quantitative results from the experiments indicate that the proposed CSSDet achieves optimal performance across a wide range of evaluation metrics, with particular strengths demonstrated in multi-scale object detection and small-size object detection. Qualitative results showcase its remarkable effectiveness in various aspects of object detection, including small object detection, objects at different scales and categories, multi-perspective scenarios, and complex scenes. …”
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Detectability of Emission from Exoplanet Outflows Calculated by pyTPCI, a New 1D Radiation-hydrodynamic Code by Riley Rosener, Michael Zhang, Jacob L. Bean

“…Photoevaporation in exoplanet atmospheres is thought to contribute to the shaping of the small planet radius valley. Escaping atmospheres have been detected in transmission across a variety of exoplanet types, from hot Jupiters to mini-Neptunes. …”
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Bioinformatics approach to processing data from high-throughput sequencing of small RNA molecules by A. A. Zharikova, Yu. V. Vyatkin, A. V. Kiseleva, A. N. Meshkov

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Characterization of small nucleolar RNA retaining transcripts in human normal and cancer cells by Guglielmo Rambaldelli, Sidra Asghar, Giulia Venturi, Federico Zacchini, Margherita Serra, Catia Giovannini, Laura Gramantieri, Marco Bernini, Alberto Inga, Erik Dassi, Lorenzo Montanaro

“…Small nucleolar RNAs are non-coding RNAs typically encoded within the introns of both protein-coding and non-coding genes. …”
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Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer by Mehran Karimzadeh, Amir Momen-Roknabadi, Taylor B. Cavazos, Yuqi Fang, Nae-Chyun Chen, Michael Multhaup, Jennifer Yen, Jeremy Ku, Jieyang Wang, Xuan Zhao, Philip Murzynowski, Kathleen Wang, Rose Hanna, Alice Huang, Diana Corti, Dang Nguyen, Ti Lam, Seda Kilinc, Patrick Arensdorf, Kimberly H. Chau, Anna Hartwig, Lisa Fish, Helen Li, Babak Behsaz, Olivier Elemento, James Zou, Fereydoun Hormozdiari, Babak Alipanahi, Hani Goodarzi

“…In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. …”
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Detection of early relapse in multiple myeloma patients by Tereza Růžičková, Monika Vlachová, Lukáš Pečinka, Monika Brychtová, Marek Večeřa, Lenka Radová, Simona Ševčíková, Marie Jarošová, Josef Havel, Luděk Pour, Sabina Ševčíková

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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…Abstract Detecting objects of varying scales, especially small ones, in Unmanned Aerial Vehicle (UAV) aerial images across diverse scenarios and viewpoints using onboard edge devices is a major challenge in computer vision. …”
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