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information and Communication Technology (ICTech), International Conference of

作者： Junfeng Qiao Chenhong Huang Aihua Zhou Hua Gu Lin Peng Xingde Huang Xiaofeng Shen Jingde Chen State grid key laboratory of information Network Security State Grid Smart Grid Research Institute Co.LTD Nanjing Jiangsu China Qingpu Power Supply Company State Grid Shanghai Electric Power Company Shanghai China

The scale of distribution network is expanding, the construction of distribution automation and ubiquitous power Internet of Things is advancing, and the multi-level and multisource real-time operation monitoring data of distribution network is growing exponentially. On the one hand, big data provides data support for distribution network reliability evaluation and dispatching planning, in different aspects, this also raises other requirements for the calculation accuracy and speed of reliability evaluation. How to mine the factors that have a great impact on reliability and make better use of electric data analysis algorithm to predict the changes of distribution network reliability in the future time scale is the focus of research. This article mainly taking technical breakthrough in electric distribution network data analysis, and studies its reliability evaluation methods of electric data analysis from both recent and forward aspects. First of all, the source of distribution network big data is analyzed from the internal and external aspects of the power system, and the data, data formats and relationships among the data source systems are analyzed. The characteristics of distribution network big data, such as wide source, diverse structure, fine granularity and complex relationships, are obtained in many ways. In view of the characteristics of multi-source and heterogeneous distribution network data, this paper puts forward a data processing algorithm based on multi-source data and a truth value discovery algorithm based on block coordinate descent method, which find out a new approach to overcome the difficulty of inconsistency and conflict of multi-source monitoring data.

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Channel Drop Out: A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI 7th

Channel Drop Out: A Simple Way to Prevent CNN from Overfitti...

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7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Luo, Jing Wang, Yaojie Xu, Rong Liu, Guangming Wang, Xiaofan Gong, Yijing Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design Xi’an China

ISBN: (纸本)9789811659393

With the development of deep learning, many motor imagery brain-computer interfaces based on convolutional neural networks (CNNs) show outstanding performances. However, the trial number of EEG in the training set is usually limited, and redundancy extensively exists in multiple channel EEG. Thus, overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of model. In this paper, channel drop out is proposed to address this problem by data augmentation and ensemble learning. Specifically, one of all EEG channels will be dropped and replaced by the mean signal of all EEG channels. In this way, the trial number in the training set was enlarged by channel drop out. And at the testing stage, all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the prediction. The experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2a to verify the performances of the proposed channel drop out approach. The results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83% and 2.65% compared with the original CNN model. So the channel drop out approach significantly improves the performances of motor imagery based BCI. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Brain computer interface

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A Study on a Reduced Indicator System for the network security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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International Conference on networking and network Applications (NaNA)

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

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time.

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Mobile Terminal security Protection Method Based on Community Detection Algorithms and Graph Matching networks 8th

Mobile Terminal Security Protection Method Based on Communit...

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8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Chen, Mu Dai, Zaojian Li, Yong Zhang, Juling Wang, Sheng Zhou, Jian Wu, Fan Institute of Information and Communication Global Energy Interconnection Research Institute Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China Electric Power Research Institute of State Grid Sichuan Chengdu610041 China State Grid Sichuan Electric Power Company Chengdu610041 China Computer Science Department Tuskegee University TuskegeeAL36088 United States

ISBN: (纸本)9783031067631

The rapid growth in the number of malware apps poses a great danger to users of power mobile terminals. Among smart devices, devices using the Android system are very popular and thus are easy targets for malware attacks. In this paper, we propose a mobile terminal security protection method based on community detection algorithms and graph matching networks. First, the call relationships between functions are extracted from the decompiled APK files, and runtime information is collected as a supplement to complete the construction of the behavior graph. Second, based on the behavior graph of malware samples, we propose a method to extract the public behavior graph as the features of malware families. Finally, a graph matching network is used to learn and calculate the similarity between the public behavior graph and the software behavior graph, and use it as the basis for detection. Our evaluation of the dataset containing 1274 malware apps and 108 benign apps shows that the selected public behavior graph is effective for Android malware detection, with an overall accuracy of 90.4%. The experimental results show that the method has a high detection rate and a low false rate, and can detect malware and its variants. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile telecommunication systems

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DHD: Double Hard Decision Decoding Scheme for NAND Flash Memory

DHD: Double Hard Decision Decoding Scheme for NAND Flash Mem...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lanlan Cui Yichuan Wang Renzhi Xiao Miao Li Xiaoxue Liu Xinhong Hei School of Computer Science and Engineering XI'AN University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Software Engineering Jiangxi University of Science and Technology Nanchang China School of Information Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

With the advancement of NAND flash technology, the increased storage density leads to intensified interference, which in turn raises the error rate during data retrieval. To ensure data reliability, low-density parity-check (LDPC) codes are extensively employed for error correction in NAND flash memory. Although LDPC soft decision decoding offers high error correction capability, it comes with a significant latency. Conversely, hard-decision decoding, although faster, lacks sufficient error correction strength. Consequently, flash memory typically initiates with hard-decision decoding and resorts to multiple soft decision decoding upon failure. To minimize decoding latency, this paper proposes a decoding mechanism based on the double hard decision, called DHD. This DHD scheme improves the Log-Likelihood Ratio (LLR) in the hard decision process. After the first hard decision fails, the read reference voltage (RRV) is adjusted to perform the second hard decision decoding. If the second hard decision also fails, soft decision decoding is then employed. Experimental results demonstrate that when the Raw Bit Error Rate (RBER) is $8.5 \times 10^{-3}$ , DHD reduces the Frame Error Rate (FER) by 86.4% compared to the traditional method.

关键词： Lead acid batteries Europe Voltage Interference Decoding Error correction Error correction codes Reliability Flash memories Iterative decoding

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KA$^{2}$2SE: key-Aggregation Authorized Searchable Encryption Scheme for Data Sharing in Wireless Sensor networks

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IEEE Transactions on Services computing 2024年第1期18卷 226-238页

作者： Haijiang Wang Jianting Ning Wei Wu Chao Lin Kai Zhang School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Data Science City University of Macau Macau China College of Education Sciences Hong Kong University of Science and Technology Guangzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China

As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “ key Forge Attack ” and “ Trapdoor Forge Attack ” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA $^security$ SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA $^security$ SE and prove its security. Finally, empirical evaluations demonstrate that KA $^security$ SE is computationally efficient in comparison with existing schemes.

关键词： Encryption Cryptography keyword search Wireless sensor networks Servers Privacy Cloud computing security Probabilistic logic Authorization

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An Abnormal Login Detection Method Based on Local Outlier Factor and Gaussian Mixture Model

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International Journal of network security 2023年第2期25卷 297-305页

作者： Guo, Wei He, Yue Chen, He-Xiong Hang, Fei-Lu Li, Yun-Jie Information Center of Yunnan Power Grid Company Limited Yunnan Kunming650034 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China

Nowadays, various enterprises have gradually begun to provide Internet services for customers. And the security of customer accounts is constantly threatened by various factors, which may result in account password disclosure and theft. Therefore, it is an important task to identify and screen users’ abnormal login behavior to protect the customers’ interests and the servers’ security. At the same time, it becomes a key security problem to extract important features from the server’s user login data and detect abnormal login behaviors. This paper proposes a method that extracts features from the login log. It reduces the dimensions of data, then calculates the outlier score of time data and IP address data after dimension reduction by adopting the local outlier factor algorithm and Gaussian mixture model, and comprehensively analyzes outlier scores. The experimental results show that this method performs high accuracy, precision ratios, and so on, around 95%, which means it can correctly calculate the corresponding outlier score according to the abnormal degree of login behavior © 2023, International Journal of network *** Rights Reserved.

关键词： Gaussian distribution

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Construction and Application of the SMART Model for Adaptive Industrial Data Collection Based on Knowledge Graphs

Construction and Application of the SMART Model for Adaptive...

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IEEE International Conference on Big Data

作者： Wendan Cheng Zhen Zhang Heng Qian Qiuyue Wang Guanqun Su Lingge Meng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shu Ju (Shandong) Intelligent Technology Co. Ltd. Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Qingniao IIoT Co. Ltd. Jinan China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Industrial data collection is the foundation for implementing enterprise digitization, which is of great significance to the development of intelligent manufacturing. However, Industrial Data Collection Standards (IDCS) are primarily published in paper or PDF formats, which makes it challenging to associate and reuse knowledge. This brings difficulties to data collection and management of heterogeneous devices, thus affecting the adaptive collection of industrial data. For this reason, this paper introduces SMART into industrial data collection and proposes the construction and application of a knowledge graph-based SMART Model. First, ontology semantic reasoning and natural language processing techniques are utilized to develop an ontology model for industrial data collection and extract fine-grained IDCS knowledge. Then, the knowledge is integrated according to the standard primitive structure and conceptual reasoning rules by constructing a standard association model to form the Industrial Data Collection Standards Knowledge Graph (IDCS-KG). Finally, the SMART competence level is quantitatively assessed based on the completion degree of each operation within the SMART Model. An experimental case study demonstrates that the SMART Model can collect intelligent adaptive industrial data through the reasoning and analysis of equipment adaptation protocols and data quality management strategies.

关键词： Adaptation models Analytical models Protocols Knowledge graphs Data collection Ontologies Data models Cognition Natural language processing Standards

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Significance-aware Medication Recommendation with Medication Representation Learning

Significance-aware Medication Recommendation with Medication...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yishuo Li Zhufeng Shao Weimin Chen Shoujin Wang Yuehan Du Wenpeng Lu 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 Mental Health Center Jinan China Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The goal of medication recommendation system is to recommend appropriate pharmaceutical interventions based on a patient’s diagnosis. Most of existing approaches often formulate these recommendations use data on diagnoses, procedures, and prescriptions accumulated in the electronic health records (EHR), and despite the great successes, they seem to have limitations on modelling the significance of medication to a patient’s current visit and mining fine-grained medication representation information. To address these issues, we propose a novel Significanceaware Medication Recommendation (SMRec) framework built on significance of medication to patients and fine-grained medication representation learning. Specifically, we first design a encoding mechanism to compute significance information of medications for each patient’s visit. Then, we utilize the set-level medication co-occurrence graph based on patients’ medical history which integrates temporal dependency to learn fine-grained medication representations. Experimental results on the publicly available MIMIC-III dataset demonstrate the superior effectiveness of our model compared to other approaches 1 .

关键词： Representation learning Drugs Federated learning Computational modeling MIMICs Encoding History

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Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction

Fusion of Dynamic Hypergraph and Clinical Event for Sequenti...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xin Zhang Xueping Peng Hongjiao Guan Long Zhao Xinxiao Qiao 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients’ future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient’s historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient’s next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods 1 .

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