2021 2nd International Conference on Computer Communication and Network Security (CCNS)最新文献_第4页

Anomaly Detection with Partially Observed Anomaly Types 部分观测异常类型的异常检测

2021 2nd International Conference on Computer Communication and Network Security (CCNS) Pub Date : 2021-07-01 DOI: 10.1109/CCNS53852.2021.00028

Wanting Zhang, Le Gao, Shaoyong Li, Wenqi Li

{"title":"Anomaly Detection with Partially Observed Anomaly Types","authors":"Wanting Zhang, Le Gao, Shaoyong Li, Wenqi Li","doi":"10.1109/CCNS53852.2021.00028","DOIUrl":"https://doi.org/10.1109/CCNS53852.2021.00028","url":null,"abstract":"In this paper, we consider the problem of anomaly detection when a small number of anomaly types are observed. Previous research primarily focused on supervised learning, when all samples are labeled. And unsupervised learning is used, when all samples are unlabeled. However, many settings do not satisfy the above two situations. Recently, there are some studies on the situations that anomalies are partially observed (e.g., Anomaly Detection with partially Observed Anomalies). It is generally believed that the anomalies are classifiable in these studies. And it is common that the types of observed anomalies cannot include all types of anomalies in the case of partially observed anomalies. We refer to this problem as anomaly detection with partially observed anomaly types and propose a two-stage anomaly detection algorithm in this condition. The proposed method in this paper is based on Anomaly Detection with partially Observed Anomalies and is available in the new setting. Experimental results demonstrate the effectiveness of the proposed method both in the case of insufficient types of observed anomalies and in the case of sufficient types of observed anomalies. Besides, anomaly detection with partially observed anomaly types avoids the use of hyper-parameter and has high generality in different datasets.","PeriodicalId":142980,"journal":{"name":"2021 2nd International Conference on Computer Communication and Network Security (CCNS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128067841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Semantic Feature Representation Method Based on Dynamic Selection of Sub-word-level and Word-level 基于子词级和词级动态选择的语义特征表示方法

2021 2nd International Conference on Computer Communication and Network Security (CCNS) Pub Date : 2021-07-01 DOI: 10.1109/CCNS53852.2021.00017

XiaoDong Cai, ZhuCheng Gao, Shuting Zheng

{"title":"A Semantic Feature Representation Method Based on Dynamic Selection of Sub-word-level and Word-level","authors":"XiaoDong Cai, ZhuCheng Gao, Shuting Zheng","doi":"10.1109/CCNS53852.2021.00017","DOIUrl":"https://doi.org/10.1109/CCNS53852.2021.00017","url":null,"abstract":"Aiming at the problem that low-frequency words or unregistered words are difficult to learn effective word-level feature information due to lack of training samples, which makes the semantic expression of text inaccurate, this paper proposes a semantic feature representation method based on sub-word-level and word-level dynamic selection. First of all, using the bidirectional Long Short-Term Memory network (Bi-LSTM) to capture the characteristics of potential long-distance dependencies, a Bi-LSTM-based sub-word feature representation method is designed on the basis of the Skip-gram method. Then, in order to accurately obtain the semantic feature representation of words, a new gated dynamic selection mechanism is designed, which combines sub-word-level and word-level feature vectors to enrich the effective information of words. The experimental results show that the method proposed in this paper is effective. Compared with the word representation method of related research, the Pearson and Spearman correlation coefficients of this method are significantly improved on the STS dataset and SICK dataset.","PeriodicalId":142980,"journal":{"name":"2021 2nd International Conference on Computer Communication and Network Security (CCNS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128137968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction model of forest fire area based on the improved Extreme Gradient Boosting 基于改进极值梯度增强的森林火灾面积预测模型

2021 2nd International Conference on Computer Communication and Network Security (CCNS) Pub Date : 2021-07-01 DOI: 10.1109/CCNS53852.2021.00011

C. Ran, Lv Fang

引用次数: 2

Encryption and verification scheme of source IPv6 address between Internet domains Internet域间源IPv6地址的加密和验证方案

2021 2nd International Conference on Computer Communication and Network Security (CCNS) Pub Date : 2021-07-01 DOI: 10.1109/CCNS53852.2021.00032

Longfei Zhen, Ke Ma

引用次数: 0

Distributed Storage and Query Method of Satellite Image Data based on HBase 基于HBase的卫星图像数据分布式存储与查询方法

2021 2nd International Conference on Computer Communication and Network Security (CCNS) Pub Date : 2021-07-01 DOI: 10.1109/CCNS53852.2021.00021

Hongjuan Liu, Jiancun Li, Wenjing Li, Wei Huang, Zhitao Shao, Yuan Zhang, Shiguang Wang, Zhaoying Yang

引用次数: 0