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PentraFormer: Learning Agents for Automated Penetration Testing via Sequence Modeling

PentraFormer: Learning Agents for Automated Penetration Test...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yunfei Wang Shixuan Liu Wenhao Wang Cheng Zhu Changjun Fan Kuihua Huang Chao Chen National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

The exponential growth of computer networks has intensified the requirement for robust security measures, rendering penetration testing—an essential practice that assesses vulnerabilities by simulating attacks—critically important. Automated penetration testing (APT) has evolved from rule-based approaches to intelligent decision-making, such as reinforcement learning (RL), better emulating the adaptability and decision-making process of human penetration testers. However, using RL for APT is challenged by the high cost of real-network interactions, the scarcity of security datasets, and the unique complexities of APT scenarios, which include long decision sequences and delayed rewards, complicating RL efficacy and convergence. Inspired by the decision Transformer’s proficiency in predicting action sequences for offline RL, coupled with its effectiveness in dealing with data efficiency and adaptability, we introduce a novel sequence-based methodology for designing APT agents. This approach is tailored to effectively manage the intricate aspects of data-scarce and complex APT scenarios. Our proposed model, PentraFormer, adeptly addresses these challenges prevalent in APT scenarios. The robustness of our model is attested to through extensive empirical evaluations conducted on APT-dedicated tasks.

关键词： Adaptation models Social computing Computational modeling decision making Transformers Robustness data models Complexity theory Security Penetration testing

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MD-HRNet: A Land use Classification Method Based on Unbalanced Samples

MD-HRNet: A Land use Classification Method Based on Unbalanc...

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International Conference on big data and Information Analytics (bigDIA)

作者： Huacheng Zhang Yong Shao Li Zhang Bowei Chen Fanquan Kong Bowen Han Jiajia Qiao Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Haikou Marine Geological Survey Center China Geological Survey Haikou China Laboratory for Big Data and Decision Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Land use/land cover classification (LULC) can reveal changes in land use patterns and assess the impact of human activities on ecosystems. This study utilizes Sentinel-2 high-resolution satellite images to classify land use in Haikou City. Due to the complexity of the actual land classes in Haikou City, which leads to sample imbalance in the visually interpreted dataset, this paper proposes an MD-HRNet model to solve the sample imbalance problem and utilizes the combination of multi-scale convolution and frequency domain to extract more information. During the experiment, the land use categories are divided into nine categories, and the experimental results show that the model classification results are better, including 43.82% for mIOU, 53.57% for recall, 55.72% for F1, and 82.99% for accuracy.

关键词： Analytical models Accuracy Urban planning data models Satellite images Reliability Resource management Sustainable development Protection Optimization

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Fine Classification of Industrial and Mining Areas in XuZhou City Using Sentinel-2 Imagery

Fine Classification of Industrial and Mining Areas in XuZhou...

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International Conference on big data and Information Analytics (bigDIA)

作者： Bowen Han Li Zhang Bowei Chen Fanquan Kong Yong Shao Yuanlin Cai Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Haikou Marine Geological Survey Center China Geological Survey Haikou China Laboratory for Big Data and Decision Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Restoring the ecological function and land value of mining areas is crucial for achieving sustainable development. The spatial distribution of fine classification in mining areas serves as a scientific basis for related policy planning and implementation, providing theoretical support for ecological restoration in these regions. This study focuses on the Chengbei mining area of Xuzhou City as the research area, utilizing Sentinel-2 imagery to establish a classification system for fine classification specific to mining areas. By coupling remote sensing image texture features, remote sensing indices, and topographic parameters, a Random Forest model was constructed to extract fine classification from the mining area and analyze the evolution of their spatial distribution patterns. The experimental results demonstrate that this model significantly enhances the efficiency and accuracy of extracting fine classification in mining areas. Following the restoration of the mining area, there is a noticeable increase in types such as arbor forests and other woodland, resulting in a more stable ecosystem conducive to the sustainable development of fine classification in the mining area.

关键词： Accuracy Graphical models Forests Biological system modeling Urban areas Feature extraction Image restoration Sustainable development Remote sensing Random forests

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Location-Based Scene Reconstruction for Long-Tail Recognition 8

Location-Based Scene Reconstruction for Long-Tail Recognitio...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Yang, Jiaxin Li, Xiaofei Zhang, Weiqi Hu, Tao Zhang, Jun Li, Shuohao Laboratory for Big Data and Decision National University of Defense Technology Changsha City China National University of Defense Technology Scientific Research Department Changsha City China The Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha City China

ISBN: (纸本)9781665450515

Real-world data often exhibit a long-tailed distribution with severe class imbalance. In such cases, the majority class dominates the deep learning training, which changes the decision boundary of the minority class and reduces the classification accuracy. In this paper, we propose a novel location-based scene reconstruction(LSR) data augmentation method for long-tail recognition. This approach uses a gradient localization method to increase the scenes of tail class samples and enhance the discrimination of the model between head and tail classes, thus the accuracy of long-tail recognition is improved. Experiments on two benchmark datasets show that the LSR method achieves state-of-the-art performance on the long-tail recognition task. More importantly, our method can be easily combined with other classification methods and improves the performance of these traditional classification methods © 2022 IEEE.

关键词： Benchmarking

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Sequential Three-Way Group decision-Making for Double Hierarchy Hesitant Fuzzy Linguistic Term Set

SSRN

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SSRN 2024年

作者： Luo, Nanfang Zhang, Qinghua Xie, Qin Wang, Yutai Yin, Longjun Wang, Guoyin Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life *** research lacks tools to fuse information quickly and interpret decision results for partially formed *** limitation is particularly noticeable when there is a need to improve the efficiency of *** address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) model is constructed via granular *** model considers the vagueness, hesitation, and variation in the double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) GDM ***, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are ***, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function at each ***, the granular structure of DHHFLTS based on sequential three-way decision (S3WD) is defined, and the decision-making strategy and interpretation of decision-level are proposed. Furthermore, the algorithm of S3W-GDM model is ***, an illustrative example are presented, and the comparative and sensitivity analyses are performed to verify the superiority of this model. © 2024, The Authors. All rights reserved.

关键词： Information fusion

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Synergies quantification and evolution on air pollutant and CO2 emissions driven by different socio-economic effects

SSRN

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SSRN 2024年

作者： Fan, Yu Jing, Siyu Zhan, Min Zhang, Jian School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Laboratory of Big Data Decision Making for Green Development Beijing100192 China School of Digital Media Engineering and Humanities Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory School of Business Central South University Changsha410083 China Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application Beijing100192 China

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results show the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOX (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOX and CO2 emissions were stronger driven by final effects structure and types, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks

arXiv

引用

arXiv 2024年

作者： Li, Ziqiang Zeng, Yueqi Xia, Pengfei Liu, Lei Fu, Zhangjie Li, Bin Nanjing University of Information Science and Technology China Big Data and Decision Lab University of Science and Technology of China China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource the labor-intensive task of data collection to third-party entities. Unfortunately, recent research has unveiled the inherent risk associated with this practice, particularly in exposing NLP systems to potential backdoor attacks. Specifically, these attacks enable malicious control over the behavior of a trained model by poisoning a small portion of the training data. Unlike backdoor attacks in computer vision, textual backdoor attacks impose stringent requirements for attack stealthiness. However, existing attack methods meet significant trade-off between effectiveness and stealthiness, largely due to the high information entropy inherent in textual data. In this paper, we introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs). Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection. Through the integration of these techniques, EST-Bad demonstrates an efficient achievement of competitive attack performance while maintaining superior stealthiness compared to prior methods across various text classifier datasets. © 2024, CC BY.

关键词： Deep neural networks

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Yb3+, Er3+ Ions Co-Doped Double Molybdate/Tungstate Composite Laser Crystals for Promising 1.5 Μm Solid State Lasers

SSRN

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SSRN 2024年

作者： Hu, Haitang Ding, Shoujun J. Wang, Miaomiao Zou, Yong Zhang, Chuancheng Ren, Hao Liu, Wenpeng School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan243002 China Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Hefei230031 China

A series of 10 at% Yb3+, 5 at% Er3+ doped NaGd(MoxW1-xO4)2 (x = 0.5, 0.7, and 0.9) single crystals were grown by Czochralski method. The structure of the crystals was determined to be a tetragonal system with space group of I41/a, as confirmed through X-ray diffraction (XRD), Rietveld refinement and FT-IR analyses. In addition, the 1.5 μm spectral properties of the crystals were investigated, including emission, absorption and fluorescence decay characteristics. Importantly, the influence of crystal composition on the spectroscopic properties was revealed deeply. As the crystal composition changes, a significant inhomogeneous broadening of the spectral and remarkable changes in energy band Eg were observed. A maximum full width at half-maximum (FWHM) at 1535 nm was obtained to be 83 nm in (MoO4)1.0(WO4)1.0 composition crystal. However, the maximum absorption and emission cross-sections were achieved in (MoO4)1.4(WO4)0.6 component crystal with values of 1.15 × 10-20 cm2 and 1.05 × 10-20 cm2, respectively. Besides, for (MoO4)1.4(WO4)0.6 composition crystal, a positive gain cross-section is obtained at around 1535 nm for a population inversion ratio β above 0.4. The results show that double molybdate/tungstate composite crystals co-doping with Yb3+ and Er3+ are promising for 1.5 μm near-infrared lasers. © 2024, The Authors. All rights reserved.

关键词： Infrared devices

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An Empirical Study on Large Language Models for Aerospace Question Answering

An Empirical Study on Large Language Models for Aerospace Qu...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Long He Lai Kang Mingyang Geng College of Systems Engineering National University of Defense Technology Changsha Hunan China Xichang Satellite Launch Center Xichang Sichuan China Laboratory for Big Data and Decision National University of Defense Technology Changsha Hunan China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The application of knowledge-based question answering (QA) systems in the aerospace sector is crucial, providing essential support in training, fault analysis, and operational manual development. These systems enhance efficiency and safety by enabling rapid, precise answers to complex technical questions, aiding in maintaining system reliability. However, limited empirical research has focused on assessing the performance of QA systems specifically tailored for aerospace, leaving an important knowledge gap in understanding their domain-specific effectiveness. This oversight may stem from the sector's unique demands for accuracy and the technical specialization required. Addressing the challenges of building an aerospace QA system requires a model with in-depth technical comprehension, able to deliver precise and contextually relevant answers across a wide range of specialized questions. Moreover, constructing a comprehensive dataset for such complex, evolving knowledge domains presents additional hurdles. This study provides an empirical assessment of three large language models—Gemma2, LLaMA, and Phi3—on a carefully curated dataset of 300 aerospace questions. Using expert human evaluation to validate model performance, we identify each model's strengths, limitations, and practical applicability in aerospace QA. Our findings highlight that while these models show promise in answering aerospace-specific questions, their accuracy, logical structure, and depth of procedural understanding vary. These insights contribute valuable guidance for future improvements in knowledge transfer, operational support, and technical training tools within the aerospace industry.

关键词： Training Analytical models Adaptation models Accuracy Large language models Question answering (information retrieval) Safety Reliability Aerospace industry Knowledge transfer

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Skill Requirements Analysis for data Analysts Based on Named Entities Recognition 2

Skill Requirements Analysis for Data Analysts Based on Named...

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2nd International Conference on big data and Informatization Education, ICBDIE 2021

作者： Cao, Lina Zhang, Jian Beijing Information Science and Technology University School of Economics and Management Beijing Intl. Sci. and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing China Beijing Information Science and Technology University School of Economics and Management Laboratory of Big Data Decision Making for Green Development Beijing China

ISBN: (纸本)9781665438704

Currently, with the surge in demand for data analysts, it is beneficial to grasp the social requirement for talents by deeply mining the job requirements of data science. This study used crawlers to acquire the job advertisements from *** and selected data science related positions. Then, the specialty and skill entities in job requirement were annotated manually, and two kinds of Named Entities Recognition models, the Bert-BiLSTM-CRF model and BiLSTM-CRF were applied to extract skill entities and specialty entities from the text data of job requirement. It was found that, the model based on Bert pre-training vector was better. At last, the characteristics of the demand from the perspective of skills and profession were analyzed. © 2021 IEEE.

关键词： Character recognition

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