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Artificial Intelligence, networking and Information technology (AINIT), International Seminar on

作者： Dawei Zhao Wenhao Sun Yang Zhou Lijuan Xu Xin Li Xiaohui Han 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

In Cyber Threat Intelligence (CTI) analysis, existing named entity recognition (NER) models often suffered a decline in performance due to their inability to adapt to the specialized terminology and complex contexts of the cybersecurity domain. To address these challenges, we introduced a novel NER model-GAT-TI-specifically optimized for CTI. By integrating a Graph Attention network (GAT) and a Hybrid Context Vector into the deep learning framework, GAT-TI significantly improved both recognition accuracy and threat entity extraction efficiency. The model utilized a pre-trained BERT model to capture semantic information and built a Dependency Parse Tree (DPT) to enhance its understanding of sentence structures, enabling more accurate entity identification. The GAT layer further refined the analysis by capturing complex dependencies among entities. Additionally, the Hybrid Context Vector merged global and local contextual features, strengthening the model's comprehension. In experiments, GAT-TI achieved an F1 score of 93.67, demonstrating its superior performance in extracting threat entities from CTI reports. In summary, GAT-TI effectively supports automated extraction and analysis of cybersecurity threat information.

关键词： Adaptation models Accuracy Terminology Named entity recognition Feature extraction Vectors Cyber threat intelligence Data mining Computer security Context modeling

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Interactive Medical Image Segmentation in Various Imaging Modalities: Current Status and Challenges

Interactive Medical Image Segmentation in Various Imaging Mo...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Huijuan Hao Wenpeng Wang Yu Chen Qingyan Dingl Fengqi Hao Hu Zhang Jinqiang Bai 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Interactive medical image segmentation plays a crucial role in medical diagnosis, treatment planning, and in-terventional procedures. With the rapid advancement of artificial intelligence and deep learning technologies, this field has not only seen significant improvements in the accuracy and efficiency of segmenting anatomical structures and pathological areas from medical images, but it has also made substantial strides in the development of user-centric interactive tools. The introduction of the Segmentation Anything Model (SAM) represents a significant expansion of prompt-driven approaches within the domain of image segmentation, introducing a plethora of previously untapped functionalities. However, due to the substantial differences between natural and medical images, the generalization capabilities of interactive segmentation models across different imaging techniques remain limited, necessitating the ongoing development of targeted interactive segmentation models. In this work, we provide a comprehensive overview aimed at extending the efficacy of SAM to various medical imaging modalities, encompassing diverse image analyses and domains. Additionally, we explore potential research directions for SAM in different medical imaging fields. Despite these advancements, the accuracy and robustness of segmentation models still largely depend on high-quality annotated data, which is costly and time-consuming to acquire. These research efforts herald significant forthcoming advancements in interactive medical image segmen-tation technology, with the potential to greatly improve patient outcomes and streamline medical procedures, thereby driving further development and innovation in the healthcare industry.

关键词： Image segmentation Analytical models Technological innovation Accuracy Image analysis Zero shot learning Streaming media Data models Robustness Medical diagnostic imaging

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Multi-Level Privacy Preserving Scheme for Visual IoT Data Based on Compressive Sensing

Multi-Level Privacy Preserving Scheme for Visual IoT Data Ba...

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International Conference on Computer Supported Cooperative Work in Design

作者： Dawei Zhao Le Ju Fenghua Tong Fuqiang Yu Xin 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 Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

With the rapid growth of IoT technology, Visual IoT (VIoT) plays a key role in areas like security surveillance and intelligent transportation, where large volumes of sensitive data are collected and transmitted. Many applications face limitations in computing and storage, such as challenges in managing traffic data. Recently, Compressed Sensing (CS) theory has been applied to improve data acquisition and processing efficiency. However, existing CS-based privacy methods focus mainly on protecting single privacy zones through key-based control and lack support for hierarchical protection across multiple sensitive regions. To address this, we propose a multi-level privacy-preserving scheme for VIoT data using CS, offering full, partial, and no access levels to meet varying security needs. We also implement watermarks to prevent key-sharing attacks among partially authorized users. Experimental results demonstrate that the scheme ensures data security while minimizing time and space overhead, making it suitable for resource-limited VIoT scenarios.

关键词： Privacy Visualization Data privacy Surveillance Transportation Watermarking Traffic control Protection Compressed sensing License plate recognition

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Apple Leaf Disease Detection Based on Yolov7 with Parameter-Free Attention Mechanism

SSRN

引用

SSRN 2023年

作者： Zhao, Minghua Han, Siqi Du, Shuangli Hu, Jing Lv, Zhiyong Wang, Lin Li, Peng School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an710048 China

China is an argicultural country, and apples are one of the most prominentfruits with high production volume. Apple leaf diseases are the major factors that aﬀect both the quantity and quality of apple production. Usually,complex background and small disease area in apple leaf images make it difcult to accurately detect leaf disease. In this paper, we propose an improvedYOLOv7 method, called YOLOv7-PFA (YOLOv7 with Parameter-Free Attention Mechanism) . Based on the YOLOv7 algorithm, to improve themodel’s detection ability for small targets and enhance the detection accuracy, we introduces the parameter-free attention mechanism SimAM (Simple,Parameter-Free Attention Module). To reduce the model parameters whileensuring the detection accuracy, the GSConv and VoVGSCSP (Vortex ofvectorized and cross stage partial network) modules are introduced. Lastly,to optimize the issue of sample imbalance in bounding box regression tasks,the model’s loss function is modifed to EIOU Loss to further enhance thedetection accuracy. Compared with the YOLOv7 algorithm, the YOLOv7-PFA algorithm increases Precision by 4.2%, Recall by 3.4%, and mAP by3.2%, while reducing the parameter amount by 14%. The experimental results show that the use of the YOLOv7-PFA algorithm can better solve theproblem of misjudgment and omission under complex backgrounds. © 2023, The Authors. All rights reserved.

关键词： Fruits

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Time Series Anomaly Detection Based on Normalized Flow and Bayesian networks 9

Time Series Anomaly Detection Based on Normalized Flow and B...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yao, Honglei Liu, Donglan Chang, Yingxian Liu, Xin Tang, Chaofan State Grid Shandong Electric Power Research Institute Jinan China State Grid Shandong Electric Power Company Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Time series anomaly detection often faces significant challenges due to the high dimensionality, interdependence, and label sparsity of the data. Traditional methods struggle to model the intricate relationships between multiple time series components and often fail to accurately detect anomalies due to insufficient data dependence mining. To address these issues, we introduce GCRNF, a novel Graph-Enhanced Standardized Flow Model that incorporates a Bayesian network for causality modeling. This model decomposes joint probabilities into conditional probabilities and utilizes a graph dependency encoder with hybrid CNN-RNN models and Graph Convolutional networks (GCNs). Our experimental results demonstrate that GCRNF outperforms existing methods in density estimation, anomaly detection, and drift identification within time series distributions. Specifically, GCRNF showed a significant improvement in detection accuracy and robustness compared to traditional methods, providing a more effective solution for identifying anomalies in complex multivariate time series data. © 2024 IEEE.

关键词： Markov processes

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Self-Evaluation of Large Language Model based on Glass-box Features

arXiv

引用

arXiv 2024年

作者： Huang, Hui Qu, Yingqi Liu, Jing Yang, Muyun Xu, Bing Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Baidu Inc. Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect – model-aware glass-box features – is overlooked. In this study, we explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output. We investigate various glass-box feature groups and discovered that the softmax distribution serves as a reliable quality indicator for self-evaluation. Experimental results on public benchmarks validate the feasibility of self-evaluation of LLMs using glass-box features1 Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Cross-chain Transaction Tracking Protocol based on Multi-dimensional Digital Watermarking Fingerprints

Cross-chain Transaction Tracking Protocol based on Multi-dim...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Duanzhen Li Shujiang Xu Haochen He Hongrui Xue Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Cross-chain technology plays a critical role in the process of interconnecting blockchains. However, all attention has been focused on how to achieve efficient cross-chain interoperability protocols, thus neglecting the most important security issues in cross-chain data interaction. Frequent network attacks and data privacy leaks have sounded the alarm for the safety of cross-chain data transactions. How to solve the problem of data ownership tracking in cross-chain transactions has become the top priority. This paper designs a cross-chain transaction tracking protocol based on multi-dimensional digital watermark fingerprints, using robust watermark, fragile watermark, and user identity key to achieve multi-dimensional tracking and supervision of cross-chain data transactions. Finally, this paper analyzes the feasibility and efficiency of the proposed protocol through experiments.

关键词： Data privacy Protocols Watermarking Fingerprint recognition Blockchains Safety Discrete wavelet transforms Interoperability

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DetecVFuzz: Enhancing security in Consumer Electronic Devices Through Scalable Vulnerability Testing of Virtual Devices

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Xiao, Xi Guo, Yongjian Jolfaei, Alireza Chen, Chuan Haghighi, Mohammad Sayad Wen, Sheng Lin, Yuanyi Tsinghua University Shenzhen International Graduate School Shenzhen China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Peng Cheng Laboratory Guangdong Shenzhen China Flinders University College of Science and Engineering SA5042 Australia Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education China Qilu University of Technology Shandong Academy of Sciences National Supercomputer Center in Jinan Jinan China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

The interconnection facilitated by consumer electronics has led to the creation of a complex network of devices. Testing efficiency of such consumer electronic devices can be improved through hardware emulation. QEMU is a classical emulator for simulation and testing. Fuzzing is a promising solution to test the security of such devices, which has become pivotal in identifying device vulnerability. However, Qtest libfuzzer, the QEMU native fuzzer, requires manual writing of stub code, which makes conducting the fuzzing process difficult and yields poor performance. To address these challenges in virtual device security analysis, we propose DetecVFuzz, a novel automatic fuzzing framework based on QEMU-Qtest, aiming to find vulnerabilities in virtual PCI devices, including USB, audio, network, etc. We inject mutated inputs into nested data structures as parameters of memory-related APIs to ensure the stable operation of VM procedures. We implemented DetecVFuzz and evaluated it on different virtual devices, such as virtual memory USB stack and drivers. In preliminary evaluations on nine recent versions of the QEMU, we found 15 new memory errors in virtual devices. In general, DetecVFuzz achieves 89.37% line coverage and 78.65% branch coverage in virtual devices. We identified 26 new crashes/errors, three of which were unseen new bugs. © 1975-2011 IEEE.

关键词： Virtual addresses

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MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive L...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huiting Li Weiyu Zhang Yong Shang Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target binding affinity prediction is critical to drug design. Some existing computational methods rely on single-scale data and cannot fully integrate the rich information of multi-scale data, such as molecular structures and network information. In this paper, we propose the MBC-DTA model, which effectively integrates multi-scale data, and models the complex interactions between atoms and amino acids. We introduce GCN and GAT, which effectively extract the feature representation of molecular structure scale and network scale. Afterwards, the features obtained from the molecular structure scale are fed into a bilinear attention network, which captures the intricate interaction information between drug-target pairs. Additionally, a cross-scale graph contrastive learning strategy is applied, optimizing the feature representations across different scales. Extensive experimental results on two benchmark datasets demonstrate that the MBC-DTA model outperforms existing state-of-the-art methods.

关键词： Drugs Computational modeling Biological system modeling Contrastive learning Benchmark testing Feature extraction Data models Bipartite graph Bioinformatics Periodic structures

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