2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第7页

Leveraging Application Complexity Partition for Android Malware Detection 利用应用程序的复杂性分区为Android恶意软件检测

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754533

Zhanbo Li, Biao Jiang, Baolei Mao, Yan Zhuang, Hongtao Zhang

{"title":"Leveraging Application Complexity Partition for Android Malware Detection","authors":"Zhanbo Li, Biao Jiang, Baolei Mao, Yan Zhuang, Hongtao Zhang","doi":"10.1109/CCIS53392.2021.9754533","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754533","url":null,"abstract":"With the widespread use of Android applications, malicious applications seriously threaten information security and personal privacy. Although a lot of researches have been conducted on malware detection by using various detection models, the effect of the complexity characteristics of Android application on the android malware detection is not investigated in depth. In this article, we leverage application complexity partition for Android malware detection to deal with different android application complexity characteristics in fine-grain. We first investigate the impact of application complexity on malware detection, and utilize application complexity to screen out four datasets with different complexity by dividing the original dataset. Then, we use frequency difference sorting (FDS) algorithm to extract highly sensitive permissions and API calls that can identify benign and malicious applications. Finally, we evaluate support vector machine (SVM) and four other machine learning methods to perform android malware detection with respect to different application complexity partitions. Experimental results show that ACPDs can achieve 95.18%-99.19% accuracy and 95.45%-99.68% recall in different application complexity datasets, which are better than the 91.02% accuracy of SigPID. The experimental results demonstrate that ACPDs are scalable enough to work well with different machine learning methods and improve machine learning based Android malware detection effectively.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675438","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

Day-ahead Prediction Method of Hourly Building Energy Consumption in Transition Season 过渡季节建筑小时能耗日前预测方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754671

Haizhou Fang, H. Tan, Ningfang Dai, Xiaolei Yuan

引用次数: 3

Cognitive Computing and Decision-Making of Traffic Intersection Based on Rule Set 基于规则集的交通交叉口认知计算与决策

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754659

Nan Zhang, Weifeng Liu, Yaning Wang

引用次数: 0

Application of Laboratory Information Management System (LIMS) in Coal Mine Electronic Products Testing Laboratory 实验室信息管理系统(LIMS)在煤矿电子产品检测实验室中的应用

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754672

Baojia Zhang, Guoming Li

引用次数: 1

Deep Name Disambiguation by Combining BERT and Orthogonal Constrained Non-negative Matrix Factorization 结合BERT和正交约束非负矩阵分解的深度名称消歧

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754675

Yangchen Huang, Licai Wang, Zhonglin Liu

{"title":"Deep Name Disambiguation by Combining BERT and Orthogonal Constrained Non-negative Matrix Factorization","authors":"Yangchen Huang, Licai Wang, Zhonglin Liu","doi":"10.1109/CCIS53392.2021.9754675","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754675","url":null,"abstract":"We are searching information on the Internet every day, with people’s name as the most popular entries. However, the ambiguity of name itself makes the returning page a mix of person entities with the same name or even non-person entities. Moreover, the scoring algorithm might rank well-known person which appears more frequently to the front, which would cover the information of others. Name disambiguation addresses these two issues by extracting discriminative features from the context and grouping the returning pages. Nevertheless, modern methods are limited by the complicated manual feature design and clustering methods, as well as the pre-defined cluster number by experience. In this work, we propose to learn the semantic representations of person name reference items with the pre-trained language model BERT incorporating triplet loss, and further group the learned features with a constrained non-negative matrix factorization algorithm. To select proper cluster number automatically, we employ the Silhouette Coefficient. Experiments on the benchmark datasets WePS show the superiority of our method in name disambiguation compared with other state-of-the-art methods.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122018552","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

CCIS 2021 Cover Page CCIS 2021封面

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/ccis53392.2021.9754618

引用次数: 0

Feature Selection Based on Genetic Algorithm With Stochastic Disturbance Local Optimization 基于随机扰动局部优化遗传算法的特征选择

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754624

Lingyun Guo, Guohe Li, Ying Li, Zheng-Feng Li

引用次数: 0

A Two-phase Constrained Multi-Objective Evolutionary Algorithm Based on the Constrained Decomposition Approach 基于约束分解方法的两阶段约束多目标进化算法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754609

Haiyang Xu, Xinye Cai, Zhenhua Li, Zhun Fan

引用次数: 0

A Multi-granularity Fusion Neural Network Model for Medical Question Classification 医学问题分类的多粒度融合神经网络模型

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754664

Yingpei Ma, Jing Wang, Y. Ren, Shuo Zhang, Runzhi Li

引用次数: 1

Website Fingerprinting Attack Through Persistent Attack of Student 通过学生的持续攻击进行网站指纹攻击

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754529

Zhen Zhu, Gang Chen, Zijiao Zhang, Mingzhu Fang, Qiyu Song, Baolei Mao

{"title":"Website Fingerprinting Attack Through Persistent Attack of Student","authors":"Zhen Zhu, Gang Chen, Zijiao Zhang, Mingzhu Fang, Qiyu Song, Baolei Mao","doi":"10.1109/CCIS53392.2021.9754529","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754529","url":null,"abstract":"Illegal users usually use Tor to hide their malicious behavior for browsing website. Website fingerprinting (WF) attack can help local network administrator to prevent illegal behavior of anonymous users. Although a lot of researches have improved website fingerprinting attacks, they still cannot address the concept drift problem effectively. In this paper, we propose a novel WF attack framework, Persistent Attack of Student (PAS), by integrating self-training mechanism with advanced deep learning (DL) related WF attack. PAS can train new DL model by using concept drift dataset with pseudo label for alleviating concept drift issue. In addition, we present a new deep convolutional neural network (DCNN) attack with stable accuracy by using automatic and local feature extraction. Then, we evaluate PAS application with different advanced deep learning WF attacks for alleviating concept drift issue. The experimental results show that DCNN attack achieves 96.50%-98.88% accuracy with 0.7-0.8x time cost of DF attack in closed world of 95-900 monitored websites, and reaches 96.32% precision and 96.31% recall in open world of 400,000 unmonitored websites. The PAS attack framework with different deep learning methods achieves 87.56%-91.46% in concept drift dataset of 56 days for 200 monitored websites, which is 2.27% 2.36% better than each original deep learning attack. The experimental results demonstrate that PAS framework can help alleviate concept drift issue effectively and DCNN can perform WF attack with less time cost efficiently.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126872991","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