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Simulating learning methodology(SLeM):an approach to machine learning automation

National Science Review 2024年第8期11卷 12-17页

作者： Zongben Xu Jun Shu Deyu Meng School of Mathematics and Statistics Xi'an Jiaotong University Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Peng Cheng Laboratory Pazhou Laboratory(Huangpu)

MACHINE LEARNING REQUIRES AUTOMATION Machine learning （ML） is a fundamental technology of artificial intelligence（AI） that focuses on searching the possibly existing mapping f:x→y to fit a given dataset D={（xi,y... 详细信息

关键词： automation learning approach machine methodology simulating slem

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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

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Computers, Materials & Continua 2025年第1期82卷 645-674页

作者： Chunhui Li Xiaoying Wang Qingjie Zhang Jiaye Liang Aijing Zhang School of Information Engineering Institute of Disaster PreventionLangfang065201China Langfang Key Laboratory of Network Emergency Protection and Network Security Langfang065201China

Previous studies have shown that deep learning is very effective in detecting known ***,when facing unknown attacks,models such as Deep Neural networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low ***,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 *** hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 *** evaluation accuracy of the new dataset GINKS2023 created in this paper is *** to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

关键词： Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm

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Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition

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Chinese Journal of Electronics 2025年第1期34卷 309-321页

作者： Hao Liu Zhiquan Feng Qingbei Guo School of Information Science and Engineering Unicersity of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Unicersity of Jinan

In contrast to the general population, behavior recognition among the elderly poses increased specificity and difficulty, rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging. Hence, this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system. The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism, innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition. By fusing four modalities, namely image, skeleton, sensor data, and audio, we further enhance the accuracy of recognition. Additionally, we introduce a novel human-robot interaction mode, where the system associates directly recognized intentions with robotic actions without explicit commands, delivering a more natural and efficient elderly assistance paradigm. This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach. Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.

关键词： Privacy Accuracy Image recognition Speech recognition Robot sensing systems Skeleton Safety Reliability Older adults Monitoring

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Heterogeneous Dual-Dynamic Attention network for Modeling Mutual Interplay of Stocks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3595-3606页

作者： Liu, Haotian Zhou, Yadong Zhou, Yuxun Hu, Bowen Xi'an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Faculty of Electronic and Information Engineering Xi'an710049 China

Modern quantitative finance and portfolio-based investment hinge on the dependence structure among financial instruments (like stocks) for return prediction, risk management, and hedging. Several attempts have been made to leverage recent advancements in graph neural networks (GNNs) to achieve effective modeling to capture stock interactions. However, existing work suffers from the homogeneity and stationary of the predefined graph structure, and their reliance on hard-coded correlation-based relationships neglects the mutual intervention of different instruments. In this study, we propose the heterogeneous dual-dynamic attention network (HD2AT) to address these challenges in modeling the mutual interplay among stocks, across four key dimensions: heterogeneity, causality, structural dynamics, and lead-lag dynamics. More specifically, our method uncovers the causal interdependencies among instruments, dynamically inferring the heterogeneous network structure from real-time trading activities. Subsequently, we identify the cross-temporal propagation of interventions by integrating interactions at the node, time, and graph levels. Extensive experiments on real-world trading data demonstrate the superiority of the proposed framework over various baselines in terms of both return prediction and back-testing profitability. © 2020 IEEE.

关键词： Commerce

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High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1139-1156页

作者： Kaili Qi Minqing Zhang Fuqiang Di Chao Jiang Key Laboratory of Network & Information Security under PAP Engineering University of PAPXi'an 710086China

Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding ***,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing ***,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding *** the data hiding phase,each encrypted image is sent to a data hider to embed the secret information *** r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be *** evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and *** last but the most important,it has higher embedding *** 512 ×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.

关键词： adaptive most significant bit(MSB)prediction password feedback secret sharing high embedding capacity reversible data hiding in encrypted images

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A Revisit to Graph Neighborhood Cardinality Estimation 40

A Revisit to Graph Neighborhood Cardinality Estimation

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Pinghui Zhang, Yuanming Cheng, Kuankuan Zhao, Junzhou Xi'An Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350317152

Graph data are ubiquitous in real-world systems such as social networks and protein-protein interaction networks. In many applications, nodes usually are associated with real-value attributes, e.g., age, income, and wealth. Recently, industry and research communities have attracted attention to mining and learning attribute graphs. In this paper, we study the problem of calculating the general neighborhood cardinality of each node v in the graph, i.e., the sum of non-negative attribute values of the nodes in the k -hop neighborhood of a node v. The naive solution is to run a k -step breadth-first-search (BFS) algorithm starting from each node and storing all visited nodes' attributes. Clearly, the time complexity of this solution is Oleft(|V| dmaxk), where |V| is the number of nodes and dmax is the maximum node degree in the graph. In real-world networks such as Twitter, dmax is over 3×106. Therefore, it is infeasible to compute the neighborhood cardinality of nodes exactly in such massive networks even if we set k=2. To solve this problem, we propose efficient methods to compute the neighborhood cardinality of graphs with non-negative node attributes and binary node attributes, respectively. Extensive experiments on large real-world networks show the efficiency and effectiveness of our methods. © 2024 IEEE.

关键词： Proteins

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CMW-Net: an adaptive robust algorithm for sample selection and label correction

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National Science Review 2023年第6期10卷 19-23页

作者： Jun Shu Xiang Yuan Deyu Meng School of Mathematics and Statistics Xi'an Jiaotong University Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM Deep neural networks (DNNs) have recently achieved impressive performance in various *** successis largely attributed to massive but carefully labeled data expected to properly and sufficiently simulate testing ***,the data collection and annotation processes are often expensiveand time consuming,making obtained training datasets in real-life applications error prone,especially with data biasfrom ideal testing distributions.

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Hierarchical Task Planning for Power Line Flow Regulation

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 29-40页

作者： Chenxi Wang Youtian Du Yanhao Huang Yuanlin Chang Zihao Guo the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong UniversityXi'an 713599China the State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China

The complexity and uncertainty in power systems cause great challenges to controlling power *** a popular data-driven technique,deep reinforcement learning(DRL)attracts attention in the control of power ***,DRL has some inherent drawbacks in terms of data efficiency and *** paper presents a novel hierarchical task planning(HTP)approach,bridging planning and DRL,to the task of power line flow ***,we introduce a threelevel task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes(TP-MDPs).Second,we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task *** addition,we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist *** results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization,a state-of-the-art deep reinforcement learning(DRL)approach,improving efficiency by 26.16%and 6.86%on both systems.

关键词： Knowledge graph power line flow regulation reinforcement learning task planning

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Weighted Attribute Based Conditional Proxy Re-Encryption in the Cloud

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Computers, Materials & Continua 2025年第4期83卷 1399-1414页

作者： Xixi Yan Jing Zhang Pengyu Cheng Henan Key Laboratory of Network Cryptography Technology Network Information Security and Cryptography LaboratorySchool of SoftwareHenan Polytechnic UniversityJiaozuo454003China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights *** introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced *** the same time,a weighted tree structure is constructed to simplify the expression of access structure *** conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal *** scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.

关键词： Cloud service conditional proxy re-encryption user revocation weighted attribute

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Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement Data leakage Android

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