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LLM-MalDetect: A Large Language Model-Based Method for Android Malware Detection

IEEE Access 2025年 13卷 81347-81364页

作者： Feng, Ruirui Chen, Hui Wang, Shuo Karim, Md Monjurul Jiang, Qingshan Hebei University College of Mathematics and Information Science Baoding071002 China Chinese Academy of Sciences Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen Polytechnic University School of Artificial Intelligence Shenzhen518055 China

Android malware poses a significant cybersecurity threat, enabling unauthorized data access, financial fraud, and device compromise. Although deep learning methods are widely used for malware detection, they often struggle with stability and adaptability in the face of evolving threats. While large language models (LLMs) have shown promise in this area, their application to Android malware detection remains underexplored, particularly with regard to optimizing the semantic relationships within Android application packages (APKs). To address this gap, we introduce LLM-MalDetect, a novel framework that improves LLM-based APK analysis by explicitly modeling semantic dependencies and leveraging structured prompt engineering for optimized detection. Our approach formalizes LLM adaptation through a robust string-based feature extraction method and a tailored fine-tuning strategy to enhance precision. Evaluations on benchmark datasets demonstrate that LLM-MalDetect achieves up to 98.97% accuracy, outperforms existing methods in terms of robustness, and enables real-time analysis. © 2013 IEEE.

关键词： Android malware

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Towards practical data alignment in production federated learning

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Frontiers of Computer science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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Matrix-based local multigranulation reduction for covering decision information systems

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International Journal of Approximate Reasoning 2025年 181卷

作者： Jiang, Tao Zhang, Yan-Lan School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China

Attribute reduction has become an essential step in pattern recognition and machine learning tasks. As an extension of the classical rough set, the covering rough set has garnered considerable attention in both theory and application. A matrix-based method for computing local covering optimistic approximation sets and local optimistic multigranulation reductions based on covering rough set in covering decision information systems (CDISs) is proposed in this paper. Firstly, we introduce a matrix representation along with its associated operations to compute the local covering optimistic approximation sets and the local positive regions of the CDISs. Subsequently, local optimistic discernibility matrices and local optimistic discernibility functions are constructed for the CDISs. By performing disjunction and conjunction operations on these local optimistic discernibility matrices, all local optimistic multigranulation reductions of the CDISs can be accurately obtained. In addition, an algorithm is developed using the local optimistic discernibility matrix to compute a suboptimal minimal local optimistic multigranulation reduction. Finally, to verify the effectiveness and feasibility of the proposed method, numerical experiments are conducted on 6 UCI datasets. © 2025 Elsevier Inc.

关键词： Covering decision information system Local optimistic multigranulation reduction Matrix

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Knowledge Transfer Across Modalities for Weakly Supervised Point Cloud Semantic Segmentation

Knowledge Transfer Across Modalities for Weakly Supervised P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zihan Wang Yunhang Shen Mengtian Li Ke Li Xing Sun Shaohui Lin Lizhuang Ma East China Normal University Tencent Youtu Lab Shanghai University Key Laboratory of Advanced Theory and Application in Statistics and Data Science

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current weakly supervised point cloud semantic segmentation struggles with insufficient utilization of limited annotations in unimodal representation learning due to the sparse and textureless nature of point clouds. In this work, we leverage cross-modality information by transferring knowledge from image and text sources to the point cloud network. The intuition is that images contribute rich texture, color, and discriminative information, complementing point clouds to boost semantic segmentation performance. To reduce extensive computational resources for cross-modality fusion, we introduce the Multi-Scale Deformable Knowledge Transfer, an innovative training scheme that optimizes and extends the one-to-one mapping to flexible one-to-many relations between multi-modal data. Furthermore, we employ pre-trained image-text models to generate pseudo labels for point clouds and construct positive and negative samples for semantic contrastive regularization, facilitating the full exploitation of unlabeled data. The experimental results evaluated on SemanticKITTI and nuScenes demonstrate substantial improvements, achieving an average gain of 3.8% over the previous weakly supervised methods, and comparable performances to fully supervised approaches.

关键词： Point cloud compression Training Representation learning Weak supervision Three-dimensional displays Semantic segmentation Semantics Signal processing Speech processing Knowledge transfer

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Origin of the tiny energy gap and Dirac points in monoclinic trilayer nickelate La4Ni3O10

arXiv

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arXiv 2025年

作者： Zhang, Hu Hebei Research Center of The Basic Discipline for Computational Physics Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory College of Physics Science and Technology Hebei University Baoding071002 China

Superconductivity was recently found in trilayer nickelate La4Ni3O10 under high pressure with a phase transition from the monoclinic P21/a structure to the tetragonal I4/mmm structure. Previous experimental works have confirmed the existence of a tiny energy gap formed with Ni 3dz2 orbitals in monoclinic La4Ni3O10. Here we investigate the physical origin of this gap by analyzing symmetry properties of energy bands based on the group theory. The tiny gap comes from energy bands with opposite parity at the Brillouin zone center. In addition, we also find previously unknown Dirac points in some momentum directions around the Fermi level. An effective Hamiltonian is constructed to describe low energy physics of the tiny energy gap and Dirac points. Due to the low crystal symmetry of monoclinic La4Ni3O10, its energy bands display strong anisotropic properties. © 2025, CC BY.

关键词： Hamiltonians

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Online multi-label streaming feature selection based on neighborhood rough set with label correlation 24

Online multi-label streaming feature selection based on neig...

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Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education

作者： Siping Pan Yaojin Lin Yu Mao Shaojie Lin School of Computer Science Minnan Normal University Zhangzhou Fujian China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou Fujian China

ISBN: (纸本)9798400712692

Online multi-label streaming feature selection has gained significant interest in high-volume data applications. Neighborhood Rough Set (NRS) has emerged as a practical tool for handling multi-label feature selection. However, the majority of existing works have been concentrated on situations where labels are treated as independent and unrelated entities, disregarding the genuine context of interdependence and correlation among labels. To address this issue, this paper introduces a novel approach for online multi-label streaming feature selection, incorporating NRS and Label Correlation (LC). In our approach, we propose the concept of strongly related label subsets based on NRS. As considering label correlation, we compute the similarity between different labels and assign different weights to each label. This integrated method enhances the effectiveness of feature selection by leveraging the interdependencies among labels. he proposed reliability of the algorithm is validated experimentally.

关键词： Label correlation

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Symmetric quantum joint measurements on multiple qubits

arXiv

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arXiv 2025年

作者： Ding, Dong He, Ying-Qiu Gao, Ting Yan, Feng-Li College of Science North China Institute of Science and Technology Beijing101601 China School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Shijiazhuang050024 China

We investigate the generalization of symmetric quantum joint measurements on multiple qubits. We first describe a method for constructing a symmetric joint measurement basis for three qubits by utilizing single-qubit states corresponding to the four vertices of a tetrahedron on the Bloch sphere. We demonstrate the expected tetrahedral symmetry of the current measurement basis and discuss its application in a trilocal star-shaped network. This architecture enables us to generalize the two-qubit symmetric joint measurement to an n-qubit version, preserving the tetrahedral or hexahedral symmetry. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Realization of zero skyrmion Hall angle by elongating skyrmions

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Physical Review B 2025年第13期111卷 134426-134426页

作者： Zhaohua Liu Shuang Li Qiyuan Zhu Xingqiang Shi Ruining Wang Jianglong Wang Hu Zhang Penglai Gong Ruqian Lian Chendong Jin Hebei Research Center of the Basic Discipline for Computational Physics Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory College of Physics Science and Technology Hebei University Baoding 071002 People's Republic of China School of Mathematical and Physical Sciences Chongqing University of Science and Technology Chongqing 401331 People's Republic of China

Magnetic skyrmions are considered to be potential information carriers in racetrack memory. However, the skyrmion Hall effect limits the maximum speed of skyrmions along the current direction, which hampers their further application. Here, we investigate the fundamental properties of an elongated skyrmion induced by an anisotropic Dzyaloshinskii-Moriya interaction (DMI). Spin-orbit torque-driven dynamics of the elongated skyrmion under various conditions, including anisotropic DMI, Gilbert damping, and current, are also performed by a combination of micromagnetic simulations and solutions of the Thiele equation. It is found that elongated skyrmions can realize a zero skyrmion Hall angle, and the gradient of Gilbert damping is an effective method to significantly control the skyrmion Hall angle. It should be noted that the rotational symmetry breaking of the elongated skyrmions results in lower thermal stability than the circular skyrmions. Our results offer the possibility of zero skyrmion Hall angle motion, which may help guide the potential application of elongated skyrmions in racetrack memory.

关键词： Anisotropy

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A Fast Framework for Efficiently Constructing Valuable Cubes 26th

A Fast Framework for Efficiently Constructing Valuable Cubes

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Li, Jingtao Gao, Bo Wan, Jianxiong Li, Leixiao Zhang, Jiaxiang School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China School of Information Engineering Beijing Institute of Graphic Communication Beijing China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People’s Republic of China Beijing China

ISBN: (纸本)9789819787975

The cube attack is a powerful technique for attacking symmetric ciphers, of which one of the core parts is the search for good cubes. The construction of candidate cubes targeting linear superpolies, proposed at Asiacrypt 2021 [20], is the dominant strategy for searching good cubes. However, the time spent on constructing candidate cubes increases significantly with initialization rounds on Trivium. To address this issue, we propose a novel framework called fast framework (The source code and data for this paper are available at https://***/Elpsys/Fast-Framework.), which enhances the efficiency of constructing candidates and discovers better cubes. The integration of numeric mapping and division property is the core of our framework;they can improve efficiency while guaranteeing accuracy. The proposed method is successfully applied to 830-and 832-round Trivium, both identifying more than 20 valuable cubes whose superpolies contain linear-independent terms and have the potential to be used in further practical key-recovery attacks. The experimental results demonstrated that the efficiency of constructing the mother cube is improved by at least a factor of 9 compared to conventional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Geometry

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FIE-Net: Foreground Instance Enhancement Network for Domain Adaptation Object Detection in Remote Sensing Imagery

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Jun Zhang, Xupeng Liu, Shuai Pan, Bin Shi, Zhenwei Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China KLMDASR LEBPS LPMC Nankai University School of Statistics and Data Science Tianjin300071 China Beihang University Image Processing Center School of Astronautics State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China

Domain adaptation methods can mitigate performance degradation in remote sensing image object detection that arises from interdomain differences. However, current approaches often overlook the focused attention on foreground features, and the differences between the foreground and the background in remote sensing images implicitly diminish the characteristics of the foreground. To address this challenge, we propose a foreground instance enhancement network (FIE-Net) to balance the differences between the foreground and the background in remote sensing images, while enhancing the alignment and application of foreground features. The FIE-Net cooperates with the foreground-focused multigranularity feature alignment (FMA) module with the label filtering and application (LFA) module, progressively focusing on the salient foreground features during the processes of feature alignment and label application. Specifically, FMA directs feature alignment toward the foreground focus during the multigranularity feature alignment process, through foreground-focus perception attention and instance-centered emphasis approach. LFA balances the foreground and background difference information contained in labels, through the hybrid threshold label filtering method and the progressive label switching strategy. The experimental results indicate superior performance and generalization capabilities of our proposed FIE-Net in multiple remote sensing adaptation scenarios. © 1980-2012 IEEE.

关键词： Optical remote sensing

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