2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)最新文献_第2页

Caching Strategy for Scalable Video Coding in Information-Centric Networking 信息中心网络中可扩展视频编码的缓存策略

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556720

Junhua Tang, S. Zhao, Yue Wu, Jianhua Li

引用次数: 1

SSIC 2018 Committee SSIC 2018委员会

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/ssic.2018.8556716

引用次数: 0

Detection for Pulmonary Nodules using RGB Channel Superposition Method in Deep Learning Framework 基于深度学习框架的RGB通道叠加方法检测肺结节

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556807

Y. Meng, P. Yi, Xuejun Guo, Wen Gu, Xin Liu, Wei Wang, Ting Zhu

引用次数: 6

An Improved Group-based Influence Maximization Method in Social Networks 一种改进的基于群体的社交网络影响力最大化方法

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556647

Danhua Huang, Li Pan

{"title":"An Improved Group-based Influence Maximization Method in Social Networks","authors":"Danhua Huang, Li Pan","doi":"10.1109/SSIC.2018.8556647","DOIUrl":"https://doi.org/10.1109/SSIC.2018.8556647","url":null,"abstract":"Influence maximization is a classic problem studied in social network analysis. This problem is NP-hard and can be solved with a greedy algorithm. However, the method requires tens of thousands of Monte-Carlo simulations, which are very time consuming and not scalable. To improve this method, researchers presented the community-based influence maximization method. However, this method detects communities based on node connections while generally ignores the influence property of nodes. In addition, when computing the influence spread based on community structure, it loses sight of the community size and border nodes. To improve the community-based influence maximization method, this paper first finds groups with similar influence characteristics based on the influence property of nodes. Then influence spread is approximately calculated based on the group structure in which the group size and border nodes are considered. Experiments demonstrate that the group-based influence maximization method in this paper achieves better influence spread than corresponding community-based influence maximization methods with matching running time.","PeriodicalId":302563,"journal":{"name":"2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)","volume":"120 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128490010","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: 4

[Copyright notice] (版权)

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/ssic.2018.8556666

引用次数: 0

PU Learning in Payload-based Web Anomaly Detection 基于有效负载的Web异常检测中的PU学习

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556662

Yuxuan Luo, Shaoyin Cheng, Chong Liu, Fan Jiang

{"title":"PU Learning in Payload-based Web Anomaly Detection","authors":"Yuxuan Luo, Shaoyin Cheng, Chong Liu, Fan Jiang","doi":"10.1109/SSIC.2018.8556662","DOIUrl":"https://doi.org/10.1109/SSIC.2018.8556662","url":null,"abstract":"Intrusion detection is one of the most important methods for protecting web-based applications. Most anomaly detection approaches have weak detection capabilities for a new type of malicious web traffic. Besides, the misuse detection methods are based on malicious pattern matching, where the patterns usually depended on security experts. Although some supervised techniques have been applied, in real scenarios, HTTP traffic dataset is impure and more diverse. In this paper, we propose a new web anomaly detection method that combines with supervised learning model and PU learning (Positive and Unlabeled learning) based on HTTP payload data. In order to represent as many data patterns as possible, we vectorize HTTP request payloads by its numeric ASCII or Unicode value on byte-level, and each HTTP payload will be represented as a dimension-fixed numerical vector. First, our approach trains a base supervised XG-Boost model to learn the most of known attack traffics, and then the remaining normal traffics will be passed to a classifier based on the PU learning algorithm for finding some unknown malicious traffics. We test our model on a dataset gathered from a well-known security enterprise and the results show that our model achieves a remarkable accuracies on known attacks detection and has a great improvement in detecting unknown malicious web traffics.","PeriodicalId":302563,"journal":{"name":"2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123585882","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: 10

An adaptive authentication and authorization scheme for IoT’s gateways: a blockchain based approach 物联网网关的自适应认证和授权方案:基于区块链的方法

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556668

Achraf Fayad, Badis Hammi, R. Khatoun

引用次数: 18

Energy-Aware Distribution of Data Fragments in Unattended Wireless Sensor Networks 无人值守无线传感器网络中数据碎片的能量感知分布

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556816

Hong-Beom Choi, Young-Bae Ko, K. Lim

{"title":"Energy-Aware Distribution of Data Fragments in Unattended Wireless Sensor Networks","authors":"Hong-Beom Choi, Young-Bae Ko, K. Lim","doi":"10.1109/SSIC.2018.8556816","DOIUrl":"https://doi.org/10.1109/SSIC.2018.8556816","url":null,"abstract":"In this paper, we study and analyze data fragment placement techniques to exploit their problems and design an energy-aware fragment placement method in Unattended Wireless Sensor Networks (UWSN). In UWSN, the survivability of data is of utmost importance because sensed data cannot be immediately transmitted and must reside in the storage of sensor nodes for extended periods of time. The data, which have delay-tolerant characteristics, can be exploited by attackers that access the storage to erase or steal the data. Even though data fragmentation methods have been designed to solve this problem, these works do not consider a critical issue of where to place data fragments in the sensor network. In this work, we show that basic existing methods of data fragment placement have some problems, especially in terms of energy efficiency and data survival probability. Based on our analysis, we propose a lightweight method that allows us to efficiently select sensor nodes that are used to safely distribute and place data fragments. We show through simulation and preliminary testbed analysis that our proposed method can improve the overall performance of data fragmentation.","PeriodicalId":302563,"journal":{"name":"2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115186870","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: 6

“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension “我的自动驾驶汽车是一头大象”:基于机器学习的难以置信维度检测器

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556651

J. Monteuuis, J. Petit, Jun Zhang, H. Labiod, Stefano Mafrica, Alain Servel

引用次数: 9

Dispatching and Distributing Energy in Energy Internet under Energy Dilemma 能源困境下的能源互联网能源调度与分配

2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC) Pub Date : 2018-10-01 DOI: 10.1109/SSIC.2018.8556650

Yunlong Hu, P. Yi, Y. Sui, Zongrui Zhang, Yao Yao, Wei Wang, Ting Zhu

{"title":"Dispatching and Distributing Energy in Energy Internet under Energy Dilemma","authors":"Yunlong Hu, P. Yi, Y. Sui, Zongrui Zhang, Yao Yao, Wei Wang, Ting Zhu","doi":"10.1109/SSIC.2018.8556650","DOIUrl":"https://doi.org/10.1109/SSIC.2018.8556650","url":null,"abstract":"The Energy Internet is a hot topic for building networks that can transmit energy like the Internet. This paper mainly discusses the composition and energy scheduling for the electric vehicle energy transmission network. The network mainly includes energy points and charging stations connected by energy routing. The energy point will generate energy and the charging station will provide energy. However, in some cases, the charging station cannot obtain energy point, which leads to its energy reserve crisis. In this paper, we introduce an algorithm to dispatch energy to alleviate the energy crisis. On the basis of some graph theory, the routing problem when energy input is lost is analyzed by comparing two algorithms from the energy point to charging station. In addition, we discuss this issue in dynamic situations to better observe changes in energy transfer over time and use real- world transportation data onto simulation. The simulation results show that the algorithm can effectively delay the emergence of crisis charging stations.","PeriodicalId":302563,"journal":{"name":"2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125027223","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: 3