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Application of meta-learning in cyberspace security:a survey

Digital Communications and Networks 2023年第1期9卷 67-78页

作者： Aimin Yang Chaomeng Lu Jie Li Xiangdong Huang Tianhao Ji Xichang Li Yichao Sheng Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshan063000China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshan063000China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshan063000China

In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in ***,these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown *** them,“one-shot learning”,“few-shot learning”,and“zero-shot learning”are challenges that cannot be ignored for traditional machine *** more intractable problem in cyberspace security is the changeable attack *** a new attack mode appears,there are few or even zero samples that can be ***-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when *** purpose is to quickly obtain a model with high accuracy and strong generalization through less data *** article first divides the meta-learning model into five research directions based on different principles of *** are model-based,metric-based,optimization-based,online-learning-based,or stacked ***,the current problems in the field of cyberspace security are categorized into three branches:cyber security,information security,and artificial intelligence security according to different ***,the application research results of various meta-learning models on these three branches are *** the same time,based on the characteristics of strong generalization,evolution,and scalability of meta-learning,we contrast and summarize its advantages in solving ***,the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.

关键词： Meta-learning Cyberspace security Machine learning Few-shot learning

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SCTAP: Supporting Scenario-Centric Trigger-Action Programming based on Software-Defined Physical Environments 23

SCTAP: Supporting Scenario-Centric Trigger-Action Programmin...

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2023 World Wide Web Conference, WWW 2023

作者： Sun, Bingkun Shen, Liwei Peng, Xin Wang, Ziming Fudan University China School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9781450394161

The physical world we live in is accelerating digitalization with the vigorous development of Internet of Things (IoT). Following this trend, Web of Things (WoT) further enables fast and efficient creation of various applications that perceive and act on the physical world using standard Web technologies. A popular way for creating WoT applications is Trigger-Action Programming (TAP), which allows users to orchestrate the capabilities of IoT devices in the form of "if trigger, then action". However, existing TAP approaches don't support scenario-centric WoT applications which involve abstract modeling of physical environments and complex spatio-temporal dependencies between events and actions. In this paper, we propose an approach called SCTAP which supports Scenario-Centric Trigger-Action Programming based on software-defined physical environments. SCTAP defines a structured and conceptual representation for physical environments, which provides the required programming abstractions for WoT applications. Based on the representation, SCTAP defines a grammar for specifying scenario-centric WoT applications with spatio-temporal dependencies. Furthermore, we design a service-based architecture for SCTAP which supports the integration of device access, event perception, environment representation, and rule execution in a loosely-coupled and extensible way. We implement SCTAP as a WoT infrastructure and evaluate it with two case studies including a smart laboratory and a smart coffee house. The results confirm the usability, feasibility and efficiency of SCTAP and its implementation. © 2023 ACM.

关键词： Internet of things

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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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Computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua Science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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ProgMate: An Intelligent Programming Assistant Based on LLM 54

ProgMate: An Intelligent Programming Assistant Based on LLM

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Zhang, Xiantao Shi, Peng Yang, Da Huang, Xuefei School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like 'omnipresence', 'adaptive guidance' and 'customization'. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education. © 2024 IEEE.

关键词： Knowledge graph

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Byzantine-robust distributed support vector machine

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Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM 54

Visualizing Program Behavior: A Study of Enhanced Program Di...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Gui, ShiJie Shi, Peng Huang, Xuefei Yang, Da Zhang, Xiaozhou Gai, Yiming School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning. © 2024 IEEE.

关键词： Students

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Multi-Level Hierarchical Dynamic Graph Convolutional Networks for Motor Imagery Eeg Analysis

SSRN

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SSRN 2023年

作者： Deng, Xin Xu, Daijiang Huo, Huaxiang Hong, Xinyi Liu, Bin The Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

The human brain network consists of tightly connected modular nodes, including local functional areas and global functional connections. Graph Convolutional Networks (GCNs) have shown impressive capabilities in learning the topological relationships among electroencephalogram (EEG) channels during motor imagery tasks. However, existing GCN methods predominantly focus on a single-level functional connection mode, overlooking the diverse connection patterns across different functional modules and neglecting local functional dependencies. To address this limitation, this paper proposes a novel approach called the Multi-level Hierarchical Dynamic GCN (MHD-GCN) method, which aims to explore the multi-layer dynamic information among EEG channels within distinct functional ***, the spatiotemporal attention module is employed to capture critical sequence fragments and spatial position information within EEG signals. Secondly, a multi-level wavelet convolutional block is utilized to capture distinct spectral features from corresponding subbands. Lastly, a pre-constructed topological space is leveraged for representation learning, comprising two branches dedicated to exploring global dynamic information and extracting local functional information, respectively. In each branch, the multi-level adjacency matrix is employed to enhance the expressive power of GCN, enabling the acquisition of discriminative features. Extensive experiments conducted on the publicly available PhysioNet dataset demonstrate that our proposed method surpasses state-of-the-art approaches in motor imagery tasks. Moreover, interpretability analysis is performed on the model, revealing active brain regions and important electrode pairs associated with movement. These findings provide valuable insights and practical guidance for future research in motor imagery tasks. © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples

arXiv

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arXiv 2024年

作者： Zhou, Ao Liu, Bin Wang, Jin Tsoumakas, Grigorios Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications China School of Informatics Aristotle University of Thessaloniki Greece

Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-batches with optimizers, where each sample is randomly selected with equal probability when constructing mini-batches. However, the intrinsic class imbalance in multi-label data may bias the model towards majority labels, since samples relevant to minority labels may be underrepresented in each mini-batch. Meanwhile, during the training process, we observe that instances associated with minority labels tend to induce greater losses. Existing heuristic batch selection methods, such as priority selection of samples with high contribution to the objective function, i.e., samples with high loss, have been proven to accelerate convergence while reducing the loss and test error in single-label data. However, batch selection methods have not yet been applied and validated in multi-label data. In this study, we introduce a simple yet effective adaptive batch selection algorithm tailored to multi-label deep learning models. It adaptively selects each batch by prioritizing hard samples related to minority labels. A variant of our method also takes informative label correlations into consideration. Comprehensive experiments combining five multi-label deep learning models on thirteen benchmark datasets show that our method converges faster and performs better than random batch selection. © 2024, CC BY.

关键词： Deep neural networks

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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