687 research outputs found
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Fame for sale: efficient detection of fake Twitter followers
are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and society. In this paper, we contribute along different
dimensions. First, we review some of the most relevant existing features and
rules (proposed by Academia and Media) for anomalous Twitter accounts
detection. Second, we create a baseline dataset of verified human and fake
follower accounts. Such baseline dataset is publicly available to the
scientific community. Then, we exploit the baseline dataset to train a set of
machine-learning classifiers built over the reviewed rules and features. Our
results show that most of the rules proposed by Media provide unsatisfactory
performance in revealing fake followers, while features proposed in the past by
Academia for spam detection provide good results. Building on the most
promising features, we revise the classifiers both in terms of reduction of
overfitting and cost for gathering the data needed to compute the features. The
final result is a novel classifier, general enough to thwart
overfitting, lightweight thanks to the usage of the less costly features, and
still able to correctly classify more than 95% of the accounts of the original
training set. We ultimately perform an information fusion-based sensitivity
analysis, to assess the global sensitivity of each of the features employed by
the classifier. The findings reported in this paper, other than being supported
by a thorough experimental methodology and interesting on their own, also pave
the way for further investigation on the novel issue of fake Twitter followers
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science
Track, Perth, Australia, 3-7 April, 2017
Integrated approach to detect spam in social media networks using hybrid features
Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques
Fake News Detection in Social Networks via Crowd Signals
Our work considers leveraging crowd signals for detecting fake news and is
motivated by tools recently introduced by Facebook that enable users to flag
fake news. By aggregating users' flags, our goal is to select a small subset of
news every day, send them to an expert (e.g., via a third-party fact-checking
organization), and stop the spread of news identified as fake by an expert. The
main objective of our work is to minimize the spread of misinformation by
stopping the propagation of fake news in the network. It is especially
challenging to achieve this objective as it requires detecting fake news with
high-confidence as quickly as possible. We show that in order to leverage
users' flags efficiently, it is crucial to learn about users' flagging
accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian
inference for detecting fake news and jointly learns about users' flagging
accuracy over time. Our algorithm employs posterior sampling to actively trade
off exploitation (selecting news that maximize the objective value at a given
epoch) and exploration (selecting news that maximize the value of information
towards learning about users' flagging accuracy). We demonstrate the
effectiveness of our approach via extensive experiments and show the power of
leveraging community signals for fake news detection
๊ฐ์ธ ์ฌํ๋ง ๋คํธ์ํฌ ๋ถ์ ๊ธฐ๋ฐ ์จ๋ผ์ธ ์ฌํ ๊ณต๊ฒฉ์ ํ์ง
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ๊น์ข
๊ถ.In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, Weibo and LinkedIn. While SNSs provide diverse benefits โ for example, fostering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with spamming in Twitter and Weibo. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) normal users, and followed a normal user. Sometimes a spammer makes link farm to increase target accounts explicit influence. Based on the assumption that the online relationships of spammers are different from those of normal users, I proposed classification schemes that detect online social attackers including spammers. I firstly focused on ego-network social relations and devised two features, structural features based on Triad Significance Profile (TSP) and relational semantic features based on hierarchical homophily in an ego-network. Experiments on real Twitter and Weibo datasets demonstrated that the proposed approach is very practical. The proposed features are scalable because instead of analyzing the whole network, they inspect user-centered ego-networks. My performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.์ต๊ทผ ์ฐ๋ฆฌ๋ Facebook, Twitter, Weibo, LinkedIn ๋ฑ์ ๋ค์ํ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๊ฐ ํญ๋ฐ์ ์ผ๋ก ์ฑ์ฅํ๋ ํ์์ ๋ชฉ๊ฒฉํ์๋ค. ํ์ง๋ง ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๊ฐ ๊ฐ์ธ๊ณผ ๊ฐ์ธ๊ฐ์ ๊ด๊ณ ๋ฐ ์ปค๋ฎค๋ํฐ ํ์ฑ๊ณผ ๋ด์ค ์ ํ ๋ฑ์ ์ฌ๋ฌ ์ด์ ์ ์ ๊ณตํด ์ฃผ๊ณ ์๋๋ฐ ๋ฐํด ๋ฐ๊ฐ์ง ์์ ํ์ ์ญ์ ๋ฐ์ํ๊ณ ์๋ค. ์คํจ๋จธ๋ค์ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๋ฅผ ๋๋ ฅ ์ผ์ ์คํธ์ ๋งค์ฐ ๋น ๋ฅด๊ณ ๋๊ฒ ์ ํํ๋ ์์ผ๋ก ์
์ฉํ๊ณ ์๋ค. ์คํธ์ ์์ ์๊ฐ ์์น ์๋ ๋ฉ์์ง๋ค์ ์ผ์ปฝ๋๋ฐ ์ด๋ ์๋น์ค์ ์ ๋ขฐ๋์ ์์ ์ฑ์ ํฌ๊ฒ ์์์ํจ๋ค. ๋ฐ๋ผ์, ์คํจ๋จธ๋ฅผ ํ์งํ๋ ๊ฒ์ด ํ์ฌ ์์
๋ฏธ๋์ด์์ ๋งค์ฐ ๊ธด๊ธํ๊ณ ์ค์ํ ๋ฌธ์ ๊ฐ ๋์๋ค. ์ด ๋
ผ๋ฌธ์ ๋ํ์ ์ธ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๋ค ์ค Twitter์ Weibo์์ ๋ฐ์ํ๋ ์คํจ๋ฐ์ ๋ค๋ฃจ๊ณ ์๋ค. ์ด๋ฌํ ์ ํ์ ์คํจ๋ฐ๋ค์ ๋ถํน์ ๋ค์์๊ฒ ๋ฉ์์ง๋ฅผ ์ ํํ๋ ๋์ ์, ๋ง์ ์ผ๋ฐ ์ฌ์ฉ์๋ค์ 'ํ๋ก์ฐ(๊ตฌ๋
)'ํ๊ณ ์ด๋ค๋ก๋ถํฐ '๋ง ํ๋ก์(๋ง ๊ตฌ๋
)'์ ์ด๋์ด ๋ด๋ ๊ฒ์ ๋ชฉ์ ์ผ๋ก ํ๊ธฐ๋ ํ๋ค. ๋๋ก๋ link farm์ ์ด์ฉํด ํน์ ๊ณ์ ์ ํ๋ก์ ์๋ฅผ ๋์ด๊ณ ๋ช
์์ ์ํฅ๋ ฅ์ ์ฆ๊ฐ์ํค๊ธฐ๋ ํ๋ค. ์คํจ๋จธ์ ์จ๋ผ์ธ ๊ด๊ณ๋ง์ด ์ผ๋ฐ ์ฌ์ฉ์์ ์จ๋ผ์ธ ์ฌํ๋ง๊ณผ ๋ค๋ฅผ ๊ฒ์ด๋ผ๋ ๊ฐ์ ํ์, ๋๋ ์คํจ๋จธ๋ค์ ํฌํจํ ์ผ๋ฐ์ ์ธ ์จ๋ผ์ธ ์ฌํ๋ง ๊ณต๊ฒฉ์๋ค์ ํ์งํ๋ ๋ถ๋ฅ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ๋๋ ๋จผ์ ๊ฐ์ธ ์ฌํ๋ง ๋ด ์ฌํ ๊ด๊ณ์ ์ฃผ๋ชฉํ๊ณ ๋ ๊ฐ์ง ์ข
๋ฅ์ ๋ถ๋ฅ ํน์ฑ์ ์ ์ํ์๋ค. ์ด๋ค์ ๊ฐ์ธ ์ฌํ๋ง์ Triad Significance Profile (TSP)์ ๊ธฐ๋ฐํ ๊ตฌ์กฐ์ ํน์ฑ๊ณผ Hierarchical homophily์ ๊ธฐ๋ฐํ ๊ด๊ณ ์๋ฏธ์ ํน์ฑ์ด๋ค. ์ค์ Twitter์ Weibo ๋ฐ์ดํฐ์
์ ๋ํ ์คํ ๊ฒฐ๊ณผ๋ ์ ์ํ ๋ฐฉ๋ฒ์ด ๋งค์ฐ ์ค์ฉ์ ์ด๋ผ๋ ๊ฒ์ ๋ณด์ฌ์ค๋ค. ์ ์ํ ํน์ฑ๋ค์ ์ ์ฒด ๋คํธ์ํฌ๋ฅผ ๋ถ์ํ์ง ์์๋ ๊ฐ์ธ ์ฌํ๋ง๋ง ๋ถ์ํ๋ฉด ๋๊ธฐ ๋๋ฌธ์ scalableํ๊ฒ ์ธก์ ๋ ์ ์๋ค. ๋์ ์ฑ๋ฅ ๋ถ์ ๊ฒฐ๊ณผ๋ ์ ์ํ ๊ธฐ๋ฒ์ด ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๋นํด true positive์ false positive ์ธก๋ฉด์์ ์ฐ์ํ๋ค๋ ๊ฒ์ ๋ณด์ฌ์ค๋ค.1 Introduction 1
2 Related Work 6
2.1 OSN Spammer Detection Approaches 6
2.1.1 Contents-based Approach 6
2.1.2 Social Network-based Approach 7
2.1.3 Subnetwork-based Approach 8
2.1.4 Behavior-based Approach 9
2.2 Link Spam Detection 10
2.3 Data mining schemes for Spammer Detection 10
2.4 Sybil Detection 12
3 Triad Significance Profile Analysis 14
3.1 Motivation 14
3.2 Twitter Dataset 18
3.3 Indegree and Outdegree of Dataset 20
3.4 Twitter spammer Detection with TSP 22
3.5 TSP-Filtering 27
3.6 Performance Evaluation of TSP-Filtering 29
4 Hierarchical Homophily Analysis 33
4.1 Motivation 33
4.2 Hierarchical Homophily in OSN 37
4.2.1 Basic Analysis of Datasets 39
4.2.2 Status gap distribution and Assortativity 44
4.2.3 Hierarchical gap distribution 49
4.3 Performance Evaluation of HH-Filtering 53
5 Overall Performance Evaluation 58
6 Conclusion 63
Bibliography 65Docto
An efficient hybrid system for anomaly detection in social networks
Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring userโs security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naรฏve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from usersโ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. ยฉ 2021, The Author(s)
Security techniques for intelligent spam sensing and anomaly detection in online social platforms
Copyright ยฉ 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen
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