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LogPal: A Generic Anomaly Detection Scheme of Heterogeneous Logs for Network Systems

security and Communication Networks 2023年第1期2023卷

作者： Sun, Lei Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

As a key resource for diagnosing and identifying problems, network syslog contains vast quantities of information. And it is the main source of data for anomaly detection of systems. Syslog presents the characteristics of large scale, diverse types and sources, data noise, and quick evolvement, which makes the detection methods not generic enough. To effectively address problem of log anomaly labelling caused by massive heterogeneous logs, we propose LogPal, a generic anomaly detection scheme of heterogeneous logs for network systems, which innovatively combines template sequences and raw log sequences to construct and generate log pattern events. By improving the self-attention mechanism of transformer, LogPal proactively synthesizes self-attention and handles log pattern events in a unique way. The model can make full use of log template and sequence semantic information, by automatically becoming aware of the pattern of logs. We implemented experiments to evaluate the performance of LogPal on publicly available datasets, and the outcome of the experiments shows that LogPal automatically adapts to log type changes and improves precision, recall, and F1 score to 99% on publicly available datasets. © 2023 Lei Sun and Xiaolong Xu.

关键词： Anomaly detection

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On Link Formation in Heterogeneous Information Networks: A View Based on Multi-label Learning

On Link Formation in Heterogeneous Information Networks: A V...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Ke-Jia Chen Shijun Xue Yun Li Bin Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9781450349932

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML 3 P is proposed. In ML 3 P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML 3 P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships.

关键词： Twitter Task analysis Feature extraction Training Measurement Correlation

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SLBERT: An Improved FLAT-BERT for Named Entity Recognition

SSRN

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SSRN 2022年

作者： Fei, Yuefan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

Named Entity Recognition (NER) aims to identify named entities mentioned in unstructured text and classify them into predefined named entity classes. Although models using word fusion have achieved relatively high performance, the recognition of ambiguous entities in text remains challenging, and the performance of the models depends on a sufficient amount of manually labeled data;exploring models that fuse more feature dimensions and schemes that fuse existing datasets is of farreaching interest. In this work, we propose an improved scheme of FLAT-BERT, SLBERT, that can be used in the field of named entity *** scheme consists of two phases that more fully exploit the global semantics of the text. In the first stage, the model is trained based on the current dataset, another dataset is predicted for cross-dataset fusion, and the correctness of label annotation is improved by entity confidence modeling. In the second stage, the global semantics of the text is extracted using Sentence BERT and embedded into the feature matrix encoded using Lattice BERT for subsequent Token classification. We have conducted experiments on several datasets, and the experimental results demonstrate the effectiveness of the proposed scheme in this paper. In terms of F1 values, compared with FLAT-BERT, the improvement is 0.15%, 0.34%, 0.61%, and 0.58% on datasets OntoNotes, MSRA, Resume, and Weibo, respectively. © 2022, The Authors. All rights reserved.

关键词： Semantics

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MNN: Mixed Nearest-Neighbors for Self-Supervised Learning

arXiv

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arXiv 2023年

作者： Long, Xianzhong Peng, Chen Li, Yun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the problem of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is an objective but often overlooked challenge due to the query of neighbor samples without supervision information. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the influence of neighbor samples on the semantics of positive samples through an intuitive weighting approach and image mixture operations. The results demonstrate that MNN exhibits exceptional generalization performance and training efficiency on four benchmark datasets. © 2023, CC BY.

关键词： Supervised learning

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Dafl: Domain Adaptation-Based Federated Learning for Privacy-Preserving Biometric Recognition

SSRN

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SSRN 2023年

作者： Wang, Zhousheng Yang, Geng Dai, Hua Bai, Yunlu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big data Security & Intelligent Processing Nanjing210023 China

With the introduction of data protection regulation in various countries, traditional centralized learning for the exploitation of sensitive biological information will gradually become unsustainable. We take face and speaker recognition systems as examples, to consider the possibility of implementing them using federated learning. It is known that federated learning methods still have some issues to be solved, e.g., parameter updates delivered can still reveal user privacy. Besides, existing privacy-preserving methods such as encryption and perturbation could pose other problems, including low accuracy and high training overhead, which can be more prominent in biometric recognition scenarios. We adopt the idea of domain adaptation and propose a new federated learning framework, DAFL, focusing on the improvement of the training efficiency and model performance, to achieve a practical privacy-preserving biometric recognition scheme for the first time. We also design a special dropout method for it to address the possible imbalance of biometric samples across users. Experiments on real-world datasets show that our approach can achieve up to 95.6% and 97.9% accuracy in face recognition and speaker recognition tasks, respectively, under medium privacy settings. Compared with existing work, it can save 56.0% of training time with a 3.1% accuracy loss in the best case. © 2023, The Authors. All rights reserved.

关键词： Biometrics

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Using deep learning to recognize biomedical entities

Using deep learning to recognize biomedical entities

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xuemin Yang Zhifei Zhang Ronggen Yang Daoyu Huang Geng Yang Lejun Gong Jiangsu Key Laboratory of Big Data Security &Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtIvE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.

关键词： Machine learning Neural networks Text mining Dictionaries Distributed databases Biological system modeling Telecommunications

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UB-CQA: A user attribute based community question answering system

UB-CQA: A user attribute based community question answering ...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Ziye Zhu Xiaoqian Liu Huakang Li Tao Li School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

intelligent Community Question Answering (CQA) system is a popular research topic in Natural Language processing (NLP) area. Recently, the development of distributed data mining has significantly improved the efficiency of the searching and sorting operations in CQA database. However, problems are left unanswered such as how to choose the best answer among multiple candidates for a single question. To solve this issue, we design a CQA system called UB-CQA. First, in database refinement part, we construct an optimized and structured database by the best answer choosing method leveraging user attribute provided by the response provider. Second, in human-computer part, the UB-CQA is able to search and provide a more satisfying answer to users by similarity calculation and re-ranking method leveraging text categorization information. Empirical evaluations show that the best answer choosing and the candidate question re-ranking methods bring great improvements in accuracy and reliability.

关键词： databases Knowledge based systems Mathematical model Knowledge discovery Internet Computer science

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Named entity recognition for Chinese telecommunications field based on Char2Vec and Bi-LSTMs

Named entity recognition for Chinese telecommunications fiel...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yu Wang Bin Xia Zheng Liu Yun Li Tao Li School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Named Entity Recognition (NER) is a basic task in Natural Language processing (NLP), which extracts the meaningful named entities from the text. Compared with the English NER, the Chinese NER is more challenge, since there is no tense in the Chinese language. Moreover, the omissions and the Internet catchwords in the Chinese corpus make the NER task more difficult. Traditional machine learning methods (e.g., CRFs) cannot address the Chinese NER effectively because they are hard to learn the complicated context in the Chinese language. To overcome the aforementioned problem, we propose a deep learning model Char2Vec+Bi-LSTMs for Chinese NER. We use the Chinese character instead of the Chinese word as the embedding unit, and the Bi-LSTMs is used to learn the complicated semantic dependency. To evaluate our proposed model, we construct the corpus from the China TELECOM FAQs. Experimental results show that our model achieves better performance than other baseline methods and the character embedding is more appropriate than the word embedding in the Chinese language.

关键词： Hidden Markov models Logic gates Computer architecture Machine learning Semantics Telecommunications

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Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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KCPMA: k-degree Contact Pattern Mining Algorithms for Moving Objects

KCPMA: k-degree Contact Pattern Mining Algorithms for Moving...

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International Conference on Computer Supported Cooperative Work in Design

作者： You Chen Hua Dai Mingfeng Jiang Qu Lu Pengyue Li Bohan Li Geng Yang Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

During infectious disease outbreaks, tracking contacted objects is important for suppressing the spread of the virus and using trajectories of moving objects to discover contacted objects is one of effective approaches. Existing algorithms focus on individual contact event discovery but lack the ability to obtain the k-degree contact events. In this paper, we propose efficient k-degree contact pattern mining algorithms that are capable of mining k-degree contact events. The definition of k-degree contact event is first formulated. Based on the definition, a sliding window-based baseline k-degree contact pattern mining algorithm (KCPMA) is presented. To improve the mining efficiency, the sample-point checking strategy and R-tree index are adopted and the optimized mining algorithm (KCPMA+) is proposed. Comprehensive experiments on real datasets demonstrate that the proposed algorithms are effective and efficient in mining k-degree contact events.

关键词： Costs Infectious diseases Federated learning Computer viruses Trajectory Indexes data mining

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