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A security Reinforcement Model for Edge computing Facilities in 5G power network 1

引用

5th International Conference on Wireless Communications and Applications, ICWCA 2021

作者： Li, Nige Zhou, Peng Shao, Zhipeng Chen, Xiaoxiao Power Grid Digitizing Technology Department State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China State Grid Zhejiang Electric Power Corporation Information and Telecommunication Branch Hangzhou China

ISBN: (数字)9789811934865

ISBN: (纸本)9789811934858

5G network has attracted much attention from the vertical industry since its commercial use. Compared with previous generations of mobile communication technologies, 5G bandwidth, delay and other communication KPI indicators have been greatly improved. 5G network has three typical application scenarios of large bandwidth, low delay and wide connection. In the power grid scenario, it can meet the needs of various power grid business access. As the key technology to realize 5G, multi-access edge computing (MEC) plays an important role in high-speed and low delay wireless data transmission network and has made indelible contributions to improving end-to-end service quality. In view of today’s increasingly complex network security environment and changing attack means, security attacks against MEC systems and applications have become endless. In order to deal with large-scale and various attacks, this paper studies the micro application security reinforcement and independent application security reinforcement technology deployed in edge computing facilities in the environment of 5G power network, proposes the security reinforcement model of MEC, and strengthens the source code of edge computing facilities based on the so file confusion technology of OLLVM, so as to ensure the safe and stable operation of MEC. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 5G mobile communication systems

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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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Edge-Based Resilient Consensus Control of Islanded Microgrids

Edge-Based Resilient Consensus Control of Islanded Microgrid...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Yixin Jiang Peiming Xu Wenqian Xu Zhihong Liang Yiwei Yang Yunan Zhang Kaitian Huang Meina Zhai CSG and Guangdong Provincial Key Laboratory of Power System Network Security Electric Power Research Institute Guangzhou Guangdong China College of Information Science and Engineering Northeastern University Shenyang China

We present a resilient output voltage recovery strategy for AC microgrids (MGs). In the presence of network attacks, distributed generators (DGs) may experience partial loss of effectiveness (PLOE) and bias faults in their actuators. These fault parameters can be heterogeneous and time-varying, posing significant challenges to voltage regulation. To address this, our approach incorporates adaptive techniques to handle the fault parameters while avoiding their direct dependence. Furthermore, we utilize boundary layers to design smooth controllers that mitigate the adverse effects of non-smooth controllers, ensuring stable and precise voltage regulation. Through extensive simulations, we validate the effectiveness of our proposed strategy in achieving reliable and robust load voltage regulation in AC MGs.

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HG-ETC: Fine-Grained Application Behaviors Classification From Encrypted network Traffic

HG-ETC: Fine-Grained Application Behaviors Classification Fr...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Weihua Liu Xiaohui Han Wenbo Zuo Yu Sun 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 Quancheng Provincial Laboratory Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The classification of encrypted traffic has garnered widespread attention from both researchers and industrial companies. However, existing application behavior-based encrypted traffic classification methods often extract limited information from flow features, typically focusing on either statistical or temporal characteristics of application behavior flows. They fail to capture higher-order features among flows, such as communication behavior characteristics. Therefore, this paper proposes a flow higher-order feature extraction model based on a self-attention hypergraph network. Specifically, we design a six-layer Temporal Convolutional network (TCN) to simultaneously learn the temporal feature representations of flows, and then fuse them together to obtain a more robust feature representation. Experimental results on two real-world datasets demonstrate that HG-ETC outperforms various state-of-the-art methods in fine-grained encrypted traffic classification tasks.

关键词： Fuses Federated learning Focusing Telecommunication traffic Forestry Feature extraction Cryptography

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From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach

From Data Integrity to Global ModeI Integrity for Decentrali...

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International Joint Conference on Neural networks (IJCNN)

作者： Na Wang Yao Zhao Youyang Qu Lei Cui Bai Li Longxiang Gao 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 School of IT Deakin University VIC Australia Zhejiang Tianheng Information Technology Company China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.

关键词： Training Federated learning Data integrity Neural networks Finance Distributed databases Organizations

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A Knowledge Distillation-Driven Lightweight CNN Model for Detecting Malicious Encrypted network Traffic

A Knowledge Distillation-Driven Lightweight CNN Model for De...

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International Joint Conference on Neural networks (IJCNN)

作者： Yuecheng Wen Xiaohui Han Wenbo Zuo Weihua Liu 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 Quancheng Provincial Laboratory Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of cybersecurity, efficiently and precisely identifying and mitigating potential threats from malicious encrypted traffic is crucial. As deep learning evolves, methods relying on Convolutional Neural networks (CNNs) to convert traffic payloads into visual representations for analysis have become popular. Although this technique effectively discerns malicious encrypted traffic, it faces inherent limitations, such as difficulties in deployment due to extensive network scales and high computational demands, especially in edge computing environments. To address these challenges, we propose a model for maliciously encrypted traffic detection, named LightMETD. The model consists of two stages. In the first stage, we utilize Random Forest (RF) with a feature selection algorithm to quickly identify easily distinguishable traffic patterns. In the second stage, the filtered traffic data is converted into grayscale images and classified using an innovative lightweight MobileNetV3-S architecture. Our LightMETD model addresses the problem of large volume and high computational requirements of CNN models. To enhance the effectiveness of the LightMETD, we propose an innovative knowledge distillation method to train LightMETD. This method significantly improves the accuracy of malicious encrypted traffic detection by designing a distillation loss function that enables the model to better capture the intricate relationships between samples. We use a USTC-TFC2016 public traffic dataset and a locally collected dataset to demonstrate the effectiveness of LightMETD. LightMETD achieves an impressive 97% average classification accuracy. LightMETD significantly outperforms other baselines in terms of volume and detection speed, underscoring its viability and superiority in addressing real-world challenges posed by malicious encrypted traffic.

关键词： Training Solid modeling Visualization Accuracy Computational modeling Telecommunication traffic Traffic control

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Joint Extraction of Entities and Relationships from Cyber Threat Intelligence based on Task-specific Fourier network

Joint Extraction of Entities and Relationships from Cyber Th...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Haiqing Lv Xiaohui Han Hui Cui Peipei Wang Wenbo Zuo Yang 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 Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The increasing complexity of cyber threats and the emergence of new attack technologies have brought huge challenges to attack incident analysis and source tracing. Using cyber threat intelligence to build a Cyber security Knowledge Graph (CKG) provides a new technical solution for attack attribution. Constructing a CKG requires numerous entity and relationship triples extracted from unstructured cyber threat intelligence texts. However, existing entity and relationship joint extraction methods in cyber threat intelligence face two problems. Firstly, they share the same word embeddings for both subtasks, ignoring the fine-grained semantic differences between the subtasks. Secondly, they rarely consider the interaction between the features of the two subtasks, which is vital for capturing the semantic dependencies between the tasks. To address these issues, we propose a joint entity and relationship extraction model specifically designed for network security concepts. We utilize two lightweight Fourier networks with independent weights to build a feature extraction module for encoding fine-grained features for entity recognition and relationship extraction tasks. Furthermore, we use a subtask feature interaction strategy assisted by a gated attention mechanism to enhance feature interaction between entity recognition and relationship extraction tasks. Use fine-grained entity recognition task information to guide relationship extraction to capture semantic dependencies between tasks. Experimental results on a cyber threat intelligence dataset demonstrate that our model outperforms existing baselines.

关键词： Attention mechanisms Face recognition Semantics Neural networks Knowledge graphs Logic gates network security

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PTGFI: A Prompt-Based Two-Stage Generative Framework for Function Name Inference

PTGFI: A Prompt-Based Two-Stage Generative Framework for Fun...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Menglu Wang Xiaohui Han Peipei Wang Wenbo Zuo 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

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of cybersecurity, analyzing malicious software or programs is crucial for preventing network attacks. Malicious code often exists in a stripped binary form to thwart analysis, presenting challenges for analysts. This study investigates inferring function names from stripped binary to aid security researchers in analyzing malicious code. We propose PTGFI, a Prompt-based Two-stage Generative framework for Function name Inference. The PTGFI framework transforms the task of inferring function names into a two-stage semantic generation problem. By capturing function descriptions of assembly functions and introducing prompt learning, effective inference of function names is achieved. In experiments, PTGFI outperforms the state-of-the-art model by 2.96 % in precision. Moreover, ablation studies demonstrate the effectiveness of advanced components within the PTGFI framework. We further validate the utility and reliability of function names generated by the PTGFI framework through case studies.

关键词： Analytical models Codes Accuracy Semantics Transforms Malware Reliability Computer crime Assembly Cybernetics

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A High-Dimensional Temporal Data Publishing Method Based on Dynamic Bayesian networks and Differential Privacy

A High-Dimensional Temporal Data Publishing Method Based on ...

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International Joint Conference on Neural networks (IJCNN)

作者： Yaxin Wang Zhen Zhang Heng Qian Yongchao Gao Qiuyue Wang 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Massive high-dimensional data generated by the Internet typically contains sensitive privacy information. Protecting data privacy while maintaining utility has become a pressing challenge. We propose a novel high-dimensional temporal data publishing method leveraging dynamic Bayesian networks and differential privacy. Initially, a dynamic Bayesian network is constructed, utilizing mutual information filtering of data. Subsequently, we calculate the Coherent Neighborhood Propinquity for each node within the network to determine edge sensitivity and establish a privacy budget. Noise is then strategically added to attribute data in accordance with the sensitivity and privacy budget requirements, ensuring the dataset complies with ε-differential privacy standards. Experimental results demonstrate that the data availability performance of the SMAP dataset (Soil Moisture Active Passive) surpasses that of competing algorithms while providing an equivalent level of privacy protection. Hence, our method significantly enhances data availability without compromising differential privacy protection.

关键词： Differential privacy Privacy Sensitivity Publishing Time series analysis Dynamic scheduling Bayes methods

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhiyao Li Aimin Li Yuechen Zhang Xiaotong Kong Wenqiang Li 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

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models.

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