DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks

@article{Gong2018DeepScanED,
  title={DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks},
  author={Qingyuan Gong and Yang Chen and Xinlei He and Zhou Zhuang and Tianyi Wang and Hong Huang and Xin Wang and Xiaoming Fu},
  journal={IEEE Communications Magazine},
  year={2018},
  volume={56},
  pages={21-27}
}
Our daily lives have been immersed in widespread location-based social networks (LBSNs. [...] Key Method Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities.Expand
Deep Learning-Based Malicious Account Detection in the Momo Social Network
TLDR
This paper explores the malicious account detection problem by introducing a deep learning-based framework using the long short-term memory (LSTM) neural network, and is able to build a classifier to achieve the binary classification. Expand
DeepFriend: finding abnormal nodes in online social networks using dynamic deep learning
TLDR
A model to classify malicious vertices using nodes' link information by training extensive features with dynamic deep learning architecture is proposed and gains higher accuracy than standard learning algorithms in the abnormal nodes’ classification. Expand
Detecting Malicious Accounts in Online Developer Communities Using Deep Learning
TLDR
This work formulated the malicious account detection problem in online developer communities, and proposed GitSec, a deep learning-based solution to detect malicious accounts, and showed that GitSec is an accurate detection system. Expand
Automatic ICA detection in online social networks with PageRank
TLDR
An automatic method to identify cloned profiles is proposed and is implemented on Hadoop framework using the MapReduce programming model, which employs a distributed processing framework, limits the search space, and decreases the required computation by clustering the profiles. Expand
Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks
TLDR
This work proposes a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs, and achieves a high prediction performance based on different social influence definitions. Expand
A Feature Based Approach to Detect Fake Profiles in Twitter
TLDR
This work aims to use a feature-based approach to identify fake profiles on social media platforms and shows that the proposed approach is efficient in detecting fake profiles. Expand
Classification of various attacks and their defence mechanism in online social networks: a survey
TLDR
A high-level classification of recent OSN attacks for recognising the problem and analysing the blow of such attacks on World Wide Web is presented and some simple-to-implement user practice tips to protect the system and user’s information are offered. Expand
Identification of Influential Users in Emerging Online Social Networks Using Cross-Site Linking
TLDR
A supervised machine learning-based system is built by leveraging the widely adopted cross-site linking function, which could overcome the limitations of referring to the user data of a single OSN. Expand
Twitter Fake Account Detection and Classification using Ontological Engineering and Semantic Web Rule Language
TLDR
A new approach with dual functions, namely to identify and classify the twitter bots based on ontological engineering and Semantic Web Rule Language (SWRL) rules, and it has been found that he ontology classifier is a more interpretable model that offers straightforward and human-interpretable decision rules, as compared to other machine learning classifiers. Expand
Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms
Online social networks (OSNs) are nowadays an indispensable tool for communication on account of their rise, simplicity, and efficacy. Worldwide users use OSN as a tool for social interactions, newsExpand
...
1
2
3
4
...

References

SHOWING 1-10 OF 14 REFERENCES
You are where you have been: Sybil detection via geo-location analysis in OSNs
TLDR
This study introduces a novel sybil detection approach by exploiting the fundamental mobility patterns that separate real users from sybil ones by incorporating the newly defined location entropy based metrics into other traditional feature sets. Expand
Detecting Rumors from Microblogs with Recurrent Neural Networks
TLDR
A novel method that learns continuous representations of microblog events for identifying rumors based on recurrent neural networks that detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services. Expand
Uncovering social network Sybils in the wild
TLDR
These efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN) are described and it is shown that Sybils can effectively avoid existing community-based Sybil detectors. Expand
Social Spammer Detection in Microblogging
TLDR
An optimization formulation is presented that models the social network and content information in a unified framework that can effectively utilize both kinds of information for social spammer detection in microblogging. Expand
Aiding the Detection of Fake Accounts in Large Scale Social Online Services
TLDR
A new tool in the hands of OSN operators, which relies on social graph properties to rank users according to their perceived likelihood of being fake (SybilRank), which is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by the Hadoop prototype. Expand
Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns
TLDR
This work presents the first large-scale analysis of restaurant reviews filtered by Dianping's fake review filtering system, and proposes some novel temporal and spatial features for supervised opinion spam detection that significantly outperform existing state-of-art features. Expand
DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
TLDR
Experimental results show that the method forecast the severely depressed mood of a user based on self-reported histories, with higher accuracy than SVM, and that the long-term historical information of a users improves the accuracy of forecasting depressed mood. Expand
"Will Check-in for Badges": Understanding Bias and Misbehavior on Location-Based Social Networks
TLDR
A crowdsourced study of Foursquare users is performed to quantify bias and misrepresentation in check-in datasets and the impact of self-selection in prior studies, and understand the motivations behind misrepresentation of check-ins, and the potential impact of any system changes designed to curtail such misbehavior. Expand
We know how you live: exploring the spectrum of urban lifestyles
TLDR
An algorithm to connect multiple social network accounts of millions of individuals and collect their publicly available heterogeneous behavioral data as well as social links is developed and a nonparametric Bayesian approach is developed to model the lifestyle spectrum of a group of individuals. Expand
Long Short-Term Memory
TLDR
A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Expand
...
1
2
...