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User Behavior to Identify Malicious Activities in Large-Scale Social Networks

Miss. Punam A. Bane
2019 International Journal for Research in Applied Science and Engineering Technology  
, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks.  ...  In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities  ...  to undertake malicious activities in a particular social network.  ... 
doi:10.22214/ijraset.2019.5069 fatcat:li6pdov2ozfvxmygadokxxdjbm

Guest editorial: social networks and social Web mining

Guandong Xu, Jeffrey Yu, Wookey Lee
2013 World wide web (Bussum)  
A prominent challenge lies in modeling and mining this vast volume of data to extract, represent and exploit meaningful knowledge, and to leverage structures and dynamics of emerging social networks residing  ...  Social networks have played an important role in different domains for about one decade, particularly involved in a broad range of social activities like user interaction, establishing friendship relationships  ...  The paper "Noisy but Non-Malicious User Detection in Social Recommender Systems", studies how to detect NNMUs in social recommender systems.  ... 
doi:10.1007/s11280-013-0254-0 fatcat:6kcexbxzezdjlmyvvljnvqwbbu

Towards Detecting Compromised Accounts on Social Networks

Manuel Egele, Gianluca Stringhini, Christopher Kruegel, Giovanni Vigna
2017 IEEE Transactions on Dependable and Secure Computing  
In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users.  ...  By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base.  ...  COMPA uses statistical models to characterize the behavior of social network users, and leverages anomaly detection techniques to identify sudden changes in their behavior.  ... 
doi:10.1109/tdsc.2015.2479616 fatcat:fpqrpuzisbhblbfxildo24ldm4

Detecting and characterizing social spam campaigns

Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen, Ben Y. Zhao
2010 Proceedings of the 10th annual conference on Internet measurement - IMC '10  
Online social networks (OSNs) are popular collaboration and communication tools for millions of users and their friends.  ...  In this paper, we present an initial study to quantify and characterize spam campaigns launched using accounts on online social networks.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1145/1879141.1879147 dblp:conf/imc/GaoHWLCZ10 fatcat:u7ywdh5bfbao5puzwv77jiluqe

Detecting and characterizing social spam campaigns

Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen, Ben Y. Zhao
2010 Proceedings of the 17th ACM conference on Computer and communications security - CCS '10  
Online social networks (OSNs) are popular collaboration and communication tools for millions of users and their friends.  ...  In this paper, we present an initial study to quantify and characterize spam campaigns launched using accounts on online social networks.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1145/1866307.1866396 dblp:conf/ccs/GaoHWLCZ10 fatcat:6iujqjayhfc4laxufalijxxw3m

Towards Detecting Compromised Accounts on Social Networks [article]

Manuel Egele, Gianluca Stringhini, Christopher Kruegel, Giovanni Vigna
2015 arXiv   pre-print
In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users.  ...  By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base.  ...  COMPA uses statistical models to characterize the behavior of social network users, and leverages anomaly detection techniques to identify sudden changes in their behavior.  ... 
arXiv:1509.03531v1 fatcat:66seuqf5h5girixsnbledylgnq

Operational Security Log Analytics for Enterprise Breach Detection

Zhou Li, Alina Oprea
2016 2016 IEEE Cybersecurity Development (SecDev)  
Compared to other research in this area, our framework analyzes multiple sources of security logs, performs large-scale analysis, and is continuously refined from feedback given by security experts.  ...  Our techniques have been successfully used in operational setting in a large organization and are currently integrated in a real-time behavior analytics product.  ...  ACKNOWLEDGEMENTS We would like to thank Ting-Fang Yen and Kaan Onarlioglu for working on the analytics framework in its early days, all members of RSA Laboratories, RSA Office of the CTO and Prof.  ... 
doi:10.1109/secdev.2016.015 dblp:conf/secdev/LiO16 fatcat:uiuxac3czvfjpj2lblw5wmhsgm

EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services

Gianluca Stringhini, Pierre Mourlanne, Grégoire Jacob, Manuel Egele, Christopher Kruegel, Giovanni Vigna
2015 USENIX Security Symposium  
., webmails and online social networks) to perform malicious activity, such as spreading malicious content or stealing sensitive information.  ...  We evaluated EVILCOHORT on multiple online services of different types (a webmail service and four online social networks), and show that it accurately identifies malicious accounts.  ...  Acknowledgments This work was supported by the Office of Naval Research (ONR) under grant N00014-12-1-0165, the Army Research Office (ARO) under grant W911NF-09-1-0553, the Department of Homeland Security  ... 
dblp:conf/uss/StringhiniMJEKV15 fatcat:uh4xxqhszfgfxotvuwojsnelfe

Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Max Ismailov, Michail Tsikerdekis, Sherali Zeadally
2020 Future Internet  
Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection.  ...  Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/fi12090148 fatcat:q4y2wb5swzaipodw72f74kni6q

Understanding user behavior in online social networks: a survey

Long Jin, Yang Chen, Tianyi Wang, Pan Hui, Athanasios V. Vasilakos
2013 IEEE Communications Magazine  
The rapid growth of OSNs has attracted a large number of researchers to explore and study this popular, ubiquitous, and large-scale service.  ...  Therefore, identifying and blocking malicious users are very important to ensure good user experience. Our survey contains four aspects of understanding user behavior in OSNs.  ... 
doi:10.1109/mcom.2013.6588663 fatcat:a6opi5hvjza5bivckays6hkv2u

Innocent by association

Yinglian Xie, Fang Yu, Qifa Ke, Martin Abadi, Eliot Gillum, Krish Vitaldevaria, Jason Walter, Junxian Huang, Zhuoqing Morley Mao
2012 Proceedings of the 2012 ACM conference on Computer and communications security - CCS '12  
In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from  ...  Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees.  ...  Other Related Work Social features have also been leveraged to defend against spamming and other social network attacks.  ... 
doi:10.1145/2382196.2382235 dblp:conf/ccs/XieYKAGVWHM12 fatcat:xnt4yaijwbbxzdi3d7kh4jjuw4

You Are How You Click: Clickstream Analysis for Sybil Detection

Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, Ben Y. Zhao
2013 USENIX Security Symposium  
We validate our clickstream models using ground-truth traces of 16,000 real and Sybil users from Renren, a large Chinese social network with 220M users.  ...  They are responsible for a growing number of threats, including fake product reviews, malware and spam on social networks, and astroturf political campaigns.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.  ... 
dblp:conf/uss/WangKWWZZ13 fatcat:2nzz5lsqjveh5d32vns5hnq3de

Proactive Insider Threat Detection through Graph Learning and Psychological Context

Oliver Brdiczka, Juan Liu, Bob Price, Jianqiang Shen, Akshay Patil, Richard Chow, Eugene Bart, Nicolas Ducheneaut
2012 2012 IEEE Symposium on Security and Privacy Workshops  
SA uses technologies including graph analysis, dynamic tracking, and machine learning to detect structural anomalies in large-scale information network data, while PP constructs dynamic psychological profiles  ...  While there are a number of existing tools that can accurately identify known attacks, these are reactive (as opposed to proactive) in their enforcement, and may be eluded by previously unseen, adversarial  ...  like to thank GLAD-PC team members Elise Weaver and Paul Sticha of HumRRO for help in understanding the psychology of adversarial insiders.  ... 
doi:10.1109/spw.2012.29 dblp:conf/sp/BrdiczkaLPSPCBD12 fatcat:4uszlerbizco5kiylzw3varm2e

DeepOSN: Bringing deep learning as malicious detection scheme in online social network

Putra Wanda, Marselina Endah Hiswati, Huang J. Jie
2020 IAES International Journal of Artificial Intelligence (IJ-AI)  
Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles.  ...  Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN.  ...  ACKNOWLEDGEMENTS This paper is conducted in the Institute of Research in Information Processing Laboratory, Harbin University of Science and Technology under CSC Scholarship.  ... 
doi:10.11591/ijai.v9.i1.pp146-154 fatcat:xeyelljk7bcmlolmr2h6foy2v4

Uncovering Social Network Sybils in the Wild [article]

Zhi Yang, Christo Wilson, Xiao Wang, Tingting Gao, Ben Y. Zhao, and Yafei Dai
2011 arXiv   pre-print
In this paper, we describe our efforts to detect, characterize and understand Sybil account activity in the Renren online social network (OSN).  ...  Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user.  ...  However, to date no large scale studies have been performed to characterize the behavior of Sybils on OSNs in the wild. Thus, the assumptions underlying these algorithms remain untested.  ... 
arXiv:1106.5321v1 fatcat:53ftje3ozrhchent4kq5qjg2ae
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