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RISS 인기검색어
- 검색키워드
- 저자 : Victor S. Sheng (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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A New Distributed Log Anomaly Detection Method based on Message Middleware and ATT-GRU
Wei Fang,Xuelei Jia,Wen Zhang,Victor S. Sheng 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.2
Logs play an important role in mastering the health of the system, experienced operation and maintenance engineer can judge which part of the system has a problem by checking the logs. In recent years, many system architectures have changed from single application to distributed application, which leads to a very huge number of logs in the system and manually check the logs to find system errors impractically. To solve the above problems, we propose a method based on Message Middleware and ATT-GRU (Attention Gate Recurrent Unit) to detect the logs anomaly of distributed systems. The works of this paper mainly include two aspects: (1) We design a high-performance distributed logs collection architecture to complete the logs collection of the distributed system. (2)We improve the existing GRU by introducing the attention mechanism to weight the key parts of the logs sequence, which can improve the training efficiency and recognition accuracy of the model to a certain extent. The results of experiments show that our method has better superiority and reliability.
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Zhaobin Liu,Wenzhi Liu,Qiang Ma,Gang Liu,Liang Zhang,Ligang Fang,Victor S. Sheng 한국통신학회 2019 Journal of communications and networks Vol.21 No.5
To address malicious attacks generated from wirelesssensor networks (WSNs), in this paper, we study the difficulty ofdetecting uncoordinated behavior by using a model that is unreliableand has uncontrollable accuracy, trustless control, and an inextensibleprotocol. A security collaboration model involving coupledstate vectors associated with topology control and time synchronizationis proposed. The networks achieve synchronizationusing weights and by controlling the number of goals. The simplecalculation of time synchronization values between neighboringnodes serves as the basis for judging the behavior of the nodetopology control. The coupling state vector calculation is the coreof the model. The topology coupling strength rate, signal intensityreduction, clock drift, and clock delay are combined to form a comprehensivemodel. The network energy consumption is reduced byupdating the coupling state vector regularly. The coupling cooperationthreshold is set to make security decisions and effectivelydistinguish between attack nodes and dead nodes. Thus, to ensurethe security and reliability of the network, we present a securitycooperation collection tree protocol (SC-CTP) scheme that maintainsa trusted environment and isolates misbehaving nodes. Thesimulation results show that the model can detect malicious nodeseffectively, has a high detection rate, and greatly reduces the energyconsumption of the whole network. In order to verify the effectivenessof the proposed model, a large-scale wireless sensor networkwith 200 nodes was deployed on a campus. The proposed modelwas applied to optimize the deployment of key nodes on the campus. Furthermore, a candidate set of these nodes were selected toachieve coupling cooperation of key goals. This test verified the reliabilityof the model, its customizable accuracy, and the reliabilityof the control.
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Cascaded-Hop For DeepFake Videos Detection
Dengyong Zhang,Pengjie Wu,Feng Li,Wenjie Zhu,Victor S. Sheng 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.5
Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.
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MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition
Jingxin Liu,Jieren Cheng,Xin Peng,Zeli Zhao,Xiangyan Tang,Victor S. Sheng 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.6
Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.
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A review of Chinese named entity recognition
( Jieren Cheng ),( Jingxin Liu ),( Xinbin Xu ),( Dongwan Xia ),( Le Liu ),( Victor S. Sheng ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.
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