Hiding in plain sight: Characterizing and detecting malicious Facebook pages

Dewan, Prateek; Bagroy, Shrey; Kumaraguru, Ponnurangam

doi:10.1109/asonam.2016.7752234

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Since the research approaches are different depending on different OSNs, selecting the most suitable platform was the first most crucial step. Many researchers have addressed the problem of detecting identity clone attacks in a single platform, and the most common platform selections were Facebook [5]- [7], Twitter, Google+ [8], and LinkedIn. Moreover, some authors have used multiple platforms such as Google+ and Twitter and Facebook as their social environments [9], [10].…”

Section: A Motivation From Previous Workmentioning

confidence: 99%

Clustered Approach for Clone Detection in Social Media

Liyanage

Premarathne

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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With the popularity of Online Social Networks (OSN), the number of different types of digital attacks has been increased causing lots of damages to their users. Identity Clone Attack (ICA) is one of the leading among them which illegally uses the information of a genuine user by duplicating it in another fake profile. These attacks severely affect a true and innocent identity since it can be misused by another malicious profile. Hence these clone profiles need to be identified and removed in order to increase the protection of users. This study introduces a model to detect clone profiles on Facebook by using a dataset that consists of profiles with attributes and network connections. Though the initial dataset was real, it was modified to make some artificial clones. The process of detection included three main stages, namely, filter by name, cluster using weighted categorical attributes and measure the strength of friend relationships among profiles, which follow one after another respectively. Finally, the list of possible clones with their percentages representing the amount of duplicability to a given victim profile was presented as the output of the model. Instead suggesting the exact clones, the representation of this duplicability percentages makes this approach more practical since there are many similar profiles but not clones. With the use of Agglomerative hierarchical clustering algorithm and Jaccard similarity measurement, a low average within cluster distance in cluster density performance and a precision of 88.75% has shown in the results. The present study highly focuses on the distribution of the dataset, where the calculation of weights for the attributes, similarity threshold and even the selection of the clustering algorithm is done based on it and this increases the adjustability of the proposed model to any other dataset. As the future improvements, this newly proposed approach can be extended to find clones of a victim on different platforms and more attributes can be considered for clustering.

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Section: A Motivation From Previous Workmentioning

confidence: 99%

Clustered Approach for Clone Detection in Social Media

Liyanage

Premarathne

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…It is probably going to assemble and examine the whole history all things considered; pages can adjustment execution after some time. To house such varieties in comportment, prescribed a self-versatile model which depends on the latest movement by the page [9].…”

Section: Related Workmentioning

confidence: 99%

Comparison of Deep Learning and Random Forest for Rumor Identification in Social Networks

Kumar

Murugeswari

Devaraj

et al. 2020

Advances in Intelligent Systems and Computing

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The societal lifetime of each individual has created with online social media. These locations have made outrageous improvements in the socialize environment. The world's targetable and fashionable Online Social Network (OSN) is Facebook, and it has brilliantly had more than a billion clients. It is a household to numerous kinds of antagonistic objects who misuse the sites by posting harmful or wrong messages. In few years, Twitter and other blogging sites have been around multimillion energetic users. It converted a novel means of rumor-spreading stage. The problem of detecting rumors is now more important, especially in OSNs. In this paper, we proposed rumor a different machine learning approaches as Naïve Bayes, Decision tree, Deep learning and Random forest algorithm for identifying rumors. The experiment can be done with Rapid miner tool on everyday data from Facebook. The schemes of rumor identification are verified by smearing fifteen sorts based on user's performances in Facebook data set to forecast whether a microblog post is a rumor or not. From the experiments, precision, recall, f-score value is calculated for all the four machine learning algorithms, further values are compared to find the accuracy (%) in all the four algorithms. And our experimental result shows that the overall average of precision for a Random forest provides 97% than the other comparative methods.

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“…Some papers used n-grams as baselines for comparisons with their handcrafted features. Others used n-grams as features to their classifiers [36]. More recent papers [37][32] used word embeddings for language modelling, mainly the ones that are constructing a classifier using unsupervised learning.…”

Section: Nlp Featuresmentioning

confidence: 99%

Can Machines Learn to Detect Fake News? A Survey Focused on Social Media

Silva¹,

Vieira²,

García³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Through a systematic literature review method, in this work we searched classical electronic libraries in order to find the most recent papers related to fake news detection on social medias. Our target is mapping the state of art of fake news detection, defining fake news and finding the most useful machine learning technique for doing so. We concluded that the most used method for automatic fake news detection is not just one classical machine learning technique, but instead a amalgamation of classic techniques coordinated by a neural network. We also identified a need for a domain ontology that would unify the different terminology and definitions of the fake news domain. This lack of consensual information may mislead opinions and conclusions.

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Hiding in plain sight: Characterizing and detecting malicious Facebook pages

Cited by 9 publications

References 12 publications

Clustered Approach for Clone Detection in Social Media

Clustered Approach for Clone Detection in Social Media

Comparison of Deep Learning and Random Forest for Rumor Identification in Social Networks

Can Machines Learn to Detect Fake News? A Survey Focused on Social Media

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