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DMF: A Deep Multimodal Fusion-Based network Traffic Classification Model

computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

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computers, Materials & Continua 2024年第10期81卷 1471-1489页

作者： Lechan Yang Li Li Shouming Ma School of Soft Engineering Jinling Institute of TechnologyNanjing211169China School of Computer Jinling Institute of TechnologyNanjing211169China School of Network Security Jinling Institute of TechnologyNanjing211169China

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban *** deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial *** this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data *** geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of *** address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input *** experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing ***,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing *** model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.

关键词： Geospatial data data recovery generative adversarial networks temporal correlation

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A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

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computer Modeling in Engineering & Sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

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Counterfactual Reasoning over Community Detection:A Case Study of the Public Science Day Community

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Journal of Social Computing 2023年第2期4卷 125-138页

作者： Wenkang Jiang Hongbo He Lei Lin Qirui Tang Runqiang Wang the Computer Network Information Center Chinese Academy of Sciences the School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100083China the Computer Network Information Center Chinese Academy of SciencesBeijing 100083China

With the rapid rise of new media platforms such as Weibo and Tiktok,communities with science communication characteristics have progressively grown on social *** communities pursue essential objectives such as increased visibility and *** the success of the public understanding of science in China,case studies of science communication communities on social media are becoming increasingly valuable as a point of *** authenticity of user influence plays an important role in the analysis of the final outcome during the process of community *** integrating counterfactual reasoning theory into a community detection algorithm,we present a novel paradigm for eliminating influence bias in online *** consider the community of Public Science Day of the Chinese Academy of Sciences as a case study to demonstrate the validity of the proposed *** addition,we examine data on science communication activities,analyze the key elements of activity communication,and provide references for not only augmenting the communication impact of similar types of popular science activities but also advancing science communication in *** main finding is that the propagation channel for the science communication experiment exhibits multi-point scattered propagation and lacks a continuous chain in the process of propagation.

关键词： causal inference counterfactual reasoning community detection science communication social networks

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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A Holistic Approach for Cyber Security Vulnerability Assessment Based on Open Source Tools: Nikto, Acunitx, ZAP, Nessus and Enhanced with AI-Powered Tool ImmuniWeb 4

A Holistic Approach for Cyber Security Vulnerability Assessm...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Sllame, Azeddien M. Tomia, Tayma Esam Rahuma, Ruqia Mohammed University of Tripoli Faculty of Information Technology Computer Network Department Libya

ISBN: (纸本)9798350372632

This paper describes a cyber security vulnerability assessment methodology powered by AI tactics. The AI-powered tool 'ImmuniWeb AI' effectively supported the tools of the suggested approach by automating a number of vulnerability management process tasks, including scanning, evaluation, prioritizing, and remediation. The recommended methodology makes use of the Nikto, Acunitx, Nessus, and ZAP proxy tools, which have been improved by the ImmuniWeb AI online tool, to quickly and effectively scan target systems in order to identify any vulnerabilities. Using such a variety of tools with a wide range of expertise practically produced a comprehensive method that can identify system vulnerabilities and generate well-written reports with appropriate remedy recommendations depending on the vulnerabilities' scores and level of danger. As a result, the outcomes showed how effective the suggested methodology. © 2024 IEEE.

关键词： Risk management

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THL: Fast Targeted High Utility Itemset Mining within Length Consideration

Journal of Network Intelligence

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Journal of network Intelligence 2025年第1期10卷 541-558页

作者： Xu, Peiming Jiang, Yixin Kuang, Xiaoyun Luo, Yu Chen, Jiahui Guangdong Provincial Key Laboratory of Power System Network Security CSG Guangzhou510000 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China

High utility itemset mining is a research topic of the pattern mining domain, which has been widely applied in reality during the past decade. Although most high utility itemset mining algorithms show significant execution performance regarding runtime and memory usage, numerous results are useless for users. Targeted high utility itemset mining can reduce the number of high utility itemsets and improve the efficiency of pattern analysis. However, the targeted high utility itemset mining task aims to find targeted high utility itemsets with as many items as possible. Users are not that interested in long results in actual scenarios. Therefore, in this article, we first formulate the problem and then address this issue by proposing a new algorithm named THL for mining targeted high utility itemset within length constraints, based on utility list structure. Furthermore, to improve the performance of the novel algorithm, THL adopts several efficiency pruning strategies, including length and utility aspects. Finally, we conduct an extensive experimental evaluation to compare the SOTA algorithms with THL on several datasets. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： data mining high utility itemset length constraint targeted items

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Deep Reinforcement Learning for Optimizing Multi-hop Distributed Collaborative Task Offloading in R2X

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9533-9548页

作者： Zhao, Wei Weng, Tangjie Cheng, Yu Liu, Zhi Kato, Nei Anhui University of Technology School of Computer Science and Technology Anhui Ma'anshan243032 China University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Tohoku University Graduate School of Information Sciences Sendai Japan

Roadside units (RSUs) with strong sensing abilities enhance the viability of the RSU-to-Everything (R2X) paradigm, offering crucial infrastructure support for Mobile Edge Computing (MEC) that enables real-time data processing and reduced delay. Since RSUs collect a large volume of data but have limited computing capability, data analysis tasks are usually offloaded to other network nodes, such as the cloud, other RSUs, or even vehicles. The multi-hop distributed collaborative task offloading scheme is expected to achieve high resource utilization efficiency and low task delay in this scenario, despite increasing energy consumption in data transmission. However, the highly dynamic nature of the R2X network topology makes it challenging for a node to independently select the next hop and collaboratively allocate tasks to neighbors in a multi-hop transmission path. Specifically, offloading decisions made by an individual node are influenced not only by its immediate neighbors but also by other nodes along the multi-hop path, referred to in this paper as the effect value. Additionally, the heterogeneity in computing resources and link delays among network nodes further increases the difficulty. To address these challenges, we first apply a Long Short-Term Memory (LSTM) model to predict and update the neighbors for each node while considering effect values, allowing them to independently adapt to environmental changes. Then, we design a two-layer Deep Reinforcement Learning (DRL) algorithm for network nodes to make decisions. The first-layer DRL algorithm is implemented by RSUs to determine task offloading modes. When an RSU decides to offload tasks to multiple vehicles for collaborative computing, the second-layer DRL algorithm is used by a vehicle to select its next hop vehicle and allocate tasks. Simulation results show that our proposed approach effectively adapts to topology changes in complex and highly dynamic network environments. Compared with existing methods,

关键词： Deep reinforcement learning

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Residual diverse ensemble for long-tailed multi-label text classification

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Science China(Information Sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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