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International Conference on computer Supported Cooperative Work in Design

作者： Yang Liu Xiaohui Han Wenbo Zuo Haiqing Lv Jing Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China Information Research Institute Qilu University of Technology(Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Advanced Persistent Threat (APT) pose an increasingly serious challenge to the security of businesses and organizations. In order to cope with these threats more effectively, effective utilization of cybers threat intelligence becomes crucial. Cyber Threat Intelligence(CTI) is characterized by fragmentation and voluminous, and information extraction can help cybersecurity researchers extract valuable information from massive cyber threat intelligence and understand the full picture of attack events. We propose a threat intelligence joint extraction model CTI-JE, which models the joint extraction problem as a table-filling task. CTI-JE uses SecBERT to generate embedding representations of words and then learns different representations for NER and RE tasks to avoid feature confusion. We construct a shared feature to enhance the interaction between subtasks, using dilated convolution and channel attention mechanism to extract fine-grained features. Experimental results on a cyber threat intelligence dataset show that our proposed model achieves better performance than existing baseline models.

关键词： Convolution Transforms Organizations Knowledge graphs Feature extraction information retrieval Vectors

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Felix: A Model of Detecting Off-chain Abnormal States in Decentralized Applications

Felix: A Model of Detecting Off-chain Abnormal States in Dec...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Lianhai Wang Qihao Huang Wei Shao Jinpeng Wang Xiaoqian Liu Fansheng Wang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the rapid development of blockchain technology, the use of decentralized applications (DApps) has experienced significant growth. However, current testing methods for DApps primarily focus on testing the smart contracts on the blockchain, which are the foundation of DApps, but lack a comprehensive approach to effectively detect off-chain abnormal states. To address this issue, this paper proposes a generic abnormal state detection model based on off-chain transaction data. The model leverages the DApp program code logic to set up test oracles to analyze off-chain transaction data. Experimental results demonstrate that the model achieves high accuracy in detecting off-chain abnormal states, with a prediction accuracy as high as 96.3%. Furthermore, a comparison with other related vulnerability detection methods shows the advantages of the proposed approach.

关键词： Analytical models Technological innovation Privacy Smart contracts Decentralized applications Data models Blockchains

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Malicious Traffic Classification for IoT based on Graph Attention network and Long Short-Term Memory network

Malicious Traffic Classification for IoT based on Graph Atte...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Lei Zhang Lizhuang Tan Huiling Shi Hongyang Sun Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

With the rapid development of digital services and the Internet of Things (IoT), the number and diversity of cyber attacks have reached unprecedented levels, making network traffic classification a crucial means of maintaining network security. However, in the face of increasingly complex traffic attacks, traditional analysis of individual traffic flows is no longer sufficient for effective detection. Therefore, to reveal the potential correlations between network traffic flows, we propose a novel IoT malicious traffic classification model called EGAT-LSTM. This model combines an improved Graph Attention network (GAT) with Long Short-Term Memory (LSTM) network to effectively capture the spatial topology and temporal features of network traffic, ultimately achieving efficient classification. Moreover, we optimize the aggregation function of network node features within the attention mechanism to effectively address the issue of essential information retention during the feature learning process. The experimental results on two publicly available IoT datasets demonstrate that our method achieves improvements in both binary and multi-class classification tasks, exhibiting significant advantages over some current approaches.

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L2fusion: Low-Light Oriented Infrared and Visible Image Fusion

L2fusion: Low-Light Oriented Infrared and Visible Image Fusi...

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IEEE International Conference on Image Processing

作者： Xiang Gao Guohua Lv Aimei Dong Zhonghe Wei Jinyong Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Infrared and visible image fusion aims to integrate salient targets and abundant texture information into a single fused image. Existing methods typically ignore the issue of illumination, so that there are problems of weak texture details and poor visual perception in case of low illumination. To address this issue, we propose a low-light oriented infrared and visible image fusion network, named L2Fusion. In particular, we first design a decomposition network according to Retinex theory to obtain the reflectance features of a visible image with low-light. Then, these features are integrated with the features extracted from the corresponding infrared image by a residual network. The finally fused image largely eliminates the negative impact caused by low illumination, and contains both salient targets and abundant texture information. Extensive experiments demonstrate the superiority of our L2Fusion over the state-of-the-art methods, in terms of both visual effect and quantitative metrics.

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Target-Aware Tracking with Long-term Context Attention

arXiv

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arXiv 2023年

作者： He, Kaijie Zhang, Canlong Xie, Sheng Li, Zhixin Wang, Zhiwen School of Computer Science and Engineering Guangxi Normal University China Guangxi Key Lab of Multi-source Information Mining and Security China School of Computer Science and Technology Guangxi University of Science and Technology China

Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above problem, we propose a long-term context attention (LCA) module that can perform extensive information fusion on the target and its context from long-term frames, and calculate the target correlation while enhancing target features. The complete contextual information contains the location of the target as well as the state around the target. LCA uses the target state from the previous frame to exclude the interference of similar objects and complex backgrounds, thus accurately locating the target and enabling the tracker to obtain higher robustness and regression accuracy. By embedding the LCA module in Transformer, we build a powerful online tracker with a target-aware backbone, termed as TATrack. In addition, we propose a dynamic online update algorithm based on the classification confidence of historical information without additional calculation burden. Our tracker achieves state-of-the-art performance on multiple benchmarks, with 71.1% AUC, 89.3% NP, and 73.0% AO on LaSOT, TrackingNet, and GOT-10k. The code and trained models are available on https://***/hekaijie123/TATrack. Copyright © 2023, The Authors. All rights reserved.

关键词： Target tracking

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enCBS: Delay Guarantee Mechanism based on Credit-based Shaping for Programmable Switch

enCBS: Delay Guarantee Mechanism based on Credit-based Shapi...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Xinhang Wang Lizhuang Tan Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The Time-Sensitive networking (TSN) aims to achieve delay guarantee through traffic shaping, and the programmable switch provides a data plane implementation solution for the flexible and deployable TSN network. However, the built-in quality of service guarantee mechanism of the current programmable switch cannot satisfy the deterministic guarantee of queue control and end-to-end delay of TSN network. Credit-based Shaping (CBS) is a common traffic shaping scheme for static TSN network, and it is helpless in the face of topology changes and dynamic entry/exit of flows. In this paper, we design the enhance CBS (enCBS), a CBS-based delay guarantee mechanism on the programmable switch, using the advantages of Software-Defined network (SDN) architecture to optimize flow management and parameter configuration. In addition, we improved the traditional CBS and strengthened the packet interval constraints. The experimental results prove that, compared with the existing quality of service control mechanism of programmable switch, the delay guarantee mechanism based on CBS shaping has better delay guarantee performance.

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Poisoning Attacks and Defenses to Learned Bloom Filters for Malicious URL Detection

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Fangming Wang, Pinghui Li, Rundong Cui, Xueyao Zhao, Junzhou Tao, Jing Zhang, Chen Guan, Xiaohong Zhejiang CreateLink Technology Co. Ltd Guangdong Guangzhou China Xi'an Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi'an710049 China Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing China

Approximate membership query (AMQ) structures represented by the Bloom Filter and its variants have been popularly researched in recent years. Researchers have recently combined machine learning with this type of structure to reduce space consumption and computation overhead further and make remarkable progress. However, with the booming performance in space or other metrics, researchers tend to ignore the security of the trained model. The machine learning model is vulnerable to poisoning attacks, and naturally, we infer that the learning-based filters also have the same deficiencies. Hence, in this paper, to confirm the inference mentioned above, experiments on the real-world datasets of URLs are conducted and prove that it is necessary to consider the security issue when using learning-based filters. We show that by data poisoning, the attacker can deflect learned Bloom Filters to make a false identification, which can lead to a significant loss in some cases. Aiming to solve this issue, we put forward a method named Defensive Learned Bloom Filter (DLBF) to diminish the influence of data poisoning and achieve a better performance compared to types of learned Bloom Filters. © 2004-2012 IEEE.

关键词： Wiener filtering

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DNSScope: Fine-Grained DNS Cache Probing for Remote network Activity Characterization

DNSScope: Fine-Grained DNS Cache Probing for Remote Network ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jianfeng Li Zheng Lin Xiaobo Ma Jianhao Li Jian Qu Xiapu Luo Xiaohong Guan MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Department of Computing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The domain name system (DNS) is indispensable to nearly every Internet service. It has been extensively utilized for network activity characterization in passive and active approaches. Compared to the passive approach, active DNS cache probing is privacy-preserving and low-cost, enabling worldwide characterization of remote network activities in different networks. Unfortunately, existing probing-based methods are too coarse-grained to characterize the time-varying features of network activities, substantially limiting their applications in time-sensitive tasks. In this paper, we advance DNSScope, a fine-grained DNS cache probing framework by tackling three challenges: sample sparsity, observational distortion, and cache entanglement. DNSScope synthesizes statistical learning and self-supervised transfer learning to achieve time-varying characterization. Extensive evaluations demonstrate that it can accurately estimate the time-varying DNS query arrival rates on recursive DNS resolvers. Its average mean absolute error is 0.124, as low as one-sixth that of the baseline methods.

关键词： Limiting Codes Accuracy Web and internet services Transfer learning Statistical learning Estimation

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A Global Task Scheduling Method Based on network Measurement and Prediction in Computing Power networks

A Global Task Scheduling Method Based on Network Measurement...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chuanfu Zhang Wen Li Jing Chen Jing Ge Di Wang Na Li Hao Sun Yudong Geng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans.

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Method and system of power load classification and decomposition based on deep learning

Method and system of power load classification and decomposi...

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Smart Grid and Energy Engineering (SGEE), International Conference on

作者： Hao Sun Hongtai Yuan Yudong Geng Jing Chen Chuanfu Zhang Wen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

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