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Large-Scale network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： network security

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A Hybrid Loss network for Localization of Image Manipulation 19th

A Hybrid Loss Network for Localization of Image Manipulation

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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Yin, Qilin Wang, Jinwei Luo, Xiangyang Nanjing University of Information Science and Technology and Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China

ISBN: (纸本)9783030694487

With the development of information security, localization of image manipulations havs become a hot topic. In this paper, a hybrid loss network is proposed for the manipulated image forensics. First, the patch prediction module extracts corresponding features representing the discrepancy of the tampered region boundaries. Then, these features are constrained by the Pixel Normalization, thereby improving the classification performance for the tampered patches. Finally, multi-scale patch prediction masks and semantic information are fused to segment out the tampered regions. The experiments demonstrate the proposed model can achieve high performance. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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Few-Shot Hyperspectral Image Classification Based on Cross-Domain Spectral Semantic Relation Transformer

Few-Shot Hyperspectral Image Classification Based on Cross-D...

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IEEE International Conference on Image Processing

作者： Mengxin Cao Guixin Zhao Aimei Dong Guohua Lv Ying Guo Xiangjun Dong 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 Faculty of Computer Science and Technology 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

In practical hyperspectral image (HSI) classification tasks, we often encounter the problems of few-shot classification and domain misalignment between source domains and target domains. To solve this classification paradigm, a meta-learning method of few-shot learning (FSL) is usually used. However, most existing FSL methods address the problem for domain alignment and neglect the exploration of semantic relationships of objects across domains. In this paper, we propose the cross-domain spectral semantic transformer FSL (SSTFSL), which can fully extract semantic features and spectral detail features for the cross-domain few-shot HSI classification task. Specifically, the multi-head self-attention (MSA) mechanism with enhancement process (EP) of the transformer is used to map out semantically relevant local regions and can enhance the ability of the model to distinguish subtle feature differences in the spectrum. In addition, the matching degree of different branches is computed by relational network learning, which ultimately enables cross-domain few-shot HSI classification. Through extensive experiments, we evaluate the classification performance of SSTFSL on HSI datasets. The results demonstrate that SSTFSL outperforms existing FSL methods and deep learning methods on HSI classification.

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Text Information Mining in Cyberspace: An Information Extraction Method Based on T5 and KeyBERT

Text Information Mining in Cyberspace: An Information Extrac...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Kuan Liu Yumei Li Yuanhua Qi Ning Qi Mengran Zhai Information Research Institute of Shandong Academy of Sciences Qilu University of Technology (Shandong Academy of Sciences) Jinan China 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) Shandong Institute of Economy and Informatization Development Jinan China 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and KeyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value.

关键词： Measurement Biological system modeling Cyberspace Text summarization Information processing Information retrieval Transformers Data models Data mining Information entropy

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gang Li Tianjiao Chen Min Li Delong Han MingLe Zhou 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 Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P 3 M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods.

关键词： Training Location awareness Visualization Noise reduction Production Feature extraction Product design Manufacturing Quality assessment Anomaly detection

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Knowledge Distillation Using Global Fusion and Feature Restoration for Industrial Defect Detectors

Knowledge Distillation Using Global Fusion and Feature Resto...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhengqian Feng Xiyao Yue Wang Li Mingle Zhou Delong Han Gang Li SHANDONG SCICOM Information and Economy Research Institute CO. . Ltd 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Deep learning technology has been widely applied in industrial quality inspection tasks to improve the accuracy of defect detection, object recognition, and classification. However, in the task of object detection, both feature-based and regression-based traditional knowledge distillation methods impose overly strict constraints on the student model. To address the above issues, this article proposes Global Fusion and Feature Restoration Knowledge Distillation(FRD). FRD integrates contextual information into the channel through a Global Fusion Module (GFM), and further utilizes attention mechanisms to adaptively focus on the distillation region after separating the foreground background. FRD also uses a Mask Feature Restoration (MFR) to mask and restore a portion of student features, improving the learnability of the model. At the same time, FRD adopts a comprehensive approach of multiple loss superposition constraints, rather than simply using MSE losses to imitate the features of teachers. Experiments have shown that FRD can effectively improve model performance in industrial defect detection tasks. On the aluminum surface defect dataset, FRD increased the mAP index of RetinaNet-Res50 from 57.7% to 61.7%. We also confirmed the effectiveness of FRD for general object detection on the Coco dataset. On a randomly selected COCO dataset containing 4000 images, FRD increased the mAP metric of RetinaNet-Res50 from 21.5% to 43.6%.

关键词： Measurement Deep learning Adaptation models Object detection Inspection Feature extraction Image restoration Object recognition Cybernetics Defect detection

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Fairness of Large Music Models: From a Culturally Diverse Perspective

Fairness of Large Music Models: From a Culturally Diverse Pe...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Qinyuan Wang Bruce Gu He Zhang Yunfeng Li School of Music University of Sydney NSW Australia 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CNPIEC KEXIN Ltd Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions.

关键词： Adaptation models Ethics Instruments Data science Rhythm Data models User experience Resonance Cultural differences Artificial intelligence

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Min Li Jinghui He Zuobin Ying Gang Li Mingle Zhou 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 Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings.

关键词： Accuracy Computational modeling Memory management Production Feature extraction Real-time systems Product design Quality assessment Reliability Anomaly detection

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

arXiv

引用

arXiv 2023年

作者： Ren, Ying Shen, Kailai Ye, Zhe Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Shandong China Juphoon System Software Co. Ltd Ningbo China

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Type-aware Enhanced Cylinder Embedding for Multi-hop Reasoning over Knowledge Graphs

Type-aware Enhanced Cylinder Embedding for Multi-hop Reasoni...

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International Joint Conference on Neural networks (IJCNN)

作者： Yong Shang Xiaoding Zhou Yan Ming Yuyan Zheng Huiting Li Weiyu 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 Shcool of Business Shandong Normal University Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Query embedding (QE) is designed to embed entities and first-order logic (FOL) queries in low-dimensional spaces, it has demonstrated considerable effectiveness in multi-hop reasoning over knowledge graphs. Recently, the practice of embedding entities and queries using geometric shapes has emerged as a promising approach, given the natural ability of these shapes to represent query answer sets and the logical relationships between them. However, existing geometry-based models, such as those employing point and box embeddings, cannot handle logical negation operations. Additionally, these models typically focus only on entities and relations, overlooking the rich semantic information available in knowledge graphs. To address these limitations, we propose the Type-aware Enhanced Cylinder Embedding (TECE) model. This model leverages three-dimensional geometric representations, integrating entity relationship type information. TECE covers a spectrum of essential First-Order Logic (FOL) operations, such as conjunctive, disjunctive, and negative. TECE improves the representation of entities and relationships in queries by incorporating semantic information. Moreover, it strengthens the model’s ability in generalization, along with deductive and inductive reasoning. Our experiment results demonstrate that TECE achieves significantly superior performance compared with existing SOTA methods on standard datasets.

关键词： Solid modeling Shape Semantics Neural networks Knowledge graphs Spread spectrum communication Cognition

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