2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第8页

CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection 基于社区注入矩阵-张量耦合分解的假新闻检测方法

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508408

Shashank Gupta, Raghuveer Thirukovalluru, Manjira Sinha, Sandya Mannarswamy

引用次数: 36

Information Requirements for National Level Cyber Situational Awareness 国家级网络态势感知的信息需求

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508410

Stefan Varga, J. Brynielsson, U. Franke

{"title":"Information Requirements for National Level Cyber Situational Awareness","authors":"Stefan Varga, J. Brynielsson, U. Franke","doi":"10.1109/ASONAM.2018.8508410","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508410","url":null,"abstract":"As modern societies become more dependent on IT services, the potential impact both of adversarial cyberattacks and non-adversarial service management mistakes grows. This calls for better cyber situational awareness-decision-makers need to know what is going on. The main focus of this paper is to examine the information elements that need to be collected and included in a common operational picture in order for stakeholders to acquire cyber situational awareness. This problem is addressed through a survey conducted among the participants of a national information assurance exercise conducted in Sweden. Most participants were government officials and employees of commercial companies that operate critical infrastructure. The results give insight into information elements that are perceived as useful, that can be contributed to and required from other organizations, which roles and stakeholders would benefit from certain information, and how the organizations work with creating cyber common operational pictures today. Among findings, it is noteworthy that adversarial behavior is not perceived as interesting, and that the respondents in general focus solely on their own organization.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131167779","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

A Framework for Data-Driven Physical Security and Insider Threat Detection 数据驱动的物理安全和内部威胁检测框架

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508599

Vasileios Mavroeidis, Kamer Vishi, A. Jøsang

{"title":"A Framework for Data-Driven Physical Security and Insider Threat Detection","authors":"Vasileios Mavroeidis, Kamer Vishi, A. Jøsang","doi":"10.1109/ASONAM.2018.8508599","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508599","url":null,"abstract":"This paper presents PSO, an ontological framework and a methodology for improving physical security and insider threat detection. PSO can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PSO can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115139417","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: 16

Link Prediction Measures in Various Types of Information Networks: A Review 不同类型信息网络中的链路预测方法综述

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508295

T. Jaya Lakshmi, S. Durga Bhavani

{"title":"Link Prediction Measures in Various Types of Information Networks: A Review","authors":"T. Jaya Lakshmi, S. Durga Bhavani","doi":"10.1109/ASONAM.2018.8508295","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508295","url":null,"abstract":"An information network is represented as a graph where nodes represent entities and edges represent interactions between nodes. There can be multiple types of nodes and edges in such networks giving rise to homogeneous, multi-relational and heterogeneous networks. Link prediction problem is defined as predicting edges that are more likely to be formed in the network at a future time. Many measures have been proposed in the literature for homogeneous networks. Extensions of many of these measures to heterogeneous networks are not available. Further, the measures need to be redefined in order to utilize the weight and time information available with the interactions. In this work, along with the logical grouping of the measures as topological, probabilistic and linear algebraic measures for all types of networks, we fill the gaps by defining the measures where ever they are not available in the literature. The empirical evaluation of each of these measures in different types of networks on the DBLP benchmark dataset is presented. An overall improvement of 12% is observed in prediction accuracy when temporal and heterogeneous information is efficiently utilized.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114807395","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

Characterizing Infrastructure Damage After Earthquake: A Split-Query Based IR Approach 地震后基础设施破坏特征:一种基于分割查询的红外方法

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508752

S. Priya, M. Bhanu, Sourav Kumar Dandapat, Kripabandhu Ghosh, Joydeep Chandra

{"title":"Characterizing Infrastructure Damage After Earthquake: A Split-Query Based IR Approach","authors":"S. Priya, M. Bhanu, Sourav Kumar Dandapat, Kripabandhu Ghosh, Joydeep Chandra","doi":"10.1109/ASONAM.2018.8508752","DOIUrl":"https://doi.org/10.1109/ASONAM.2018.8508752","url":null,"abstract":"Retrieving relevant information from social media based on specific requirements has become a focus area for researchers. In this paper, we propose a framework for online retrieval of tweets providing information about possible infrastructure damages, caused due to earthquakes and use the same to determine a damage score for the possibly affected locations. Identifying such tweets would not only provide a holistic view of the affected areas but would also help in taking necessary relief actions. Existing works on this topic fail to effectively capture the semantic variation in the tweets, possibly due to poor content quality, thereby providing scopes for further improvement in the mechanisms involved. Our proposed technique relies on a novel split-query based mechanism along with a pseudo-relevance feedback approach to identify the relevant tweets. The pseudo-relevance feedback approach expands on an initial set of seed tweets obtained using a semi-automatic query generation mechanism that couples topic based clustering with human annotation. Empirical validation of our proposed method on a manually annotated ground truth data reveals a considerable improvement in precision, recall and mean average precision over several baseline methods.","PeriodicalId":135949,"journal":{"name":"2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131828462","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: 7

This Paper is About Lexical Propagation on Twitter. H*ckin Smart. 12/10. Would Accept! 这篇论文是关于Twitter上的词汇传播。H* kin Smart, 12/10。会接受!

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508445

J. Golbeck, C. Buntain

引用次数: 0

Development of a Social Network for Research Support and Individual Well-Being Improvement 研究支持与个人福祉改善之社会网路发展

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508365

Lucas V. A. Caldas, A. Jacob, S. S. C. Silva, F. Pontes, F. Lobato

引用次数: 1

Shortest Path Distance Approximation Using Deep Learning Techniques 使用深度学习技术的最短路径距离逼近

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508763

Fatemeh Salehi Rizi, Jörg Schlötterer, M. Granitzer

引用次数: 25

Smart and Connected Water Resource Management Via Social Media and Community Engagement 通过社交媒体和社区参与进行智能互联水资源管理

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508602

L. Nguyen, R. Hewett, A. Namin, N. Alvarez, Cristina Bradatan, Fang Jin

引用次数: 7

SiNA: A Scalable Iterative Network Aligner 一个可扩展的迭代网络对齐器

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2018-08-01 DOI: 10.1109/ASONAM.2018.8508468

Abdurrahman Yasar, B. Uçar, Ümit V. Çatalyürek

引用次数: 1