Malicious accounts: Dark of the social networks

Adewole, Kayode S.; Anuar, Nor Badrul; Kamsin, Amirrudin; Varathan, Kasturi Dewi; Razak, Syed Abdul

doi:10.1016/j.jnca.2016.11.030

Cited by 137 publications

(47 citation statements)

References 96 publications

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“…Supervised ML approaches are unable to find zero-day malicious bots Adewole et al (2017). Indeed, they need a labelled dataset that captures the features and the behaviors of a diverse set of bots.…”

Section: Supervised Machine Learning Approachesmentioning

confidence: 99%

“…This method is derived from cross validation in which a subset of the available data is kept out, and used for testing on N number of fold. It is also worth noting that some features are computationally expensive to extract from large OSNs Adewole et al (2017). Interestingly Cresci et al (2015) showed that the best performing features are also the most costly ones.…”

Section: Supervised Machine Learning Approachesmentioning

confidence: 99%

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Detection of malicious social bots: A survey and a refined taxonomy

Latah

2020

Expert Systems with Applications

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Section: Supervised Machine Learning Approachesmentioning

confidence: 99%

Section: Supervised Machine Learning Approachesmentioning

confidence: 99%

Detection of malicious social bots: A survey and a refined taxonomy

Latah

2020

Expert Systems with Applications

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“…Spam tweets can change Twitter statistics and lead to false information sharing and false bilateral collaborations [13]. For example, it is important to determine-in terms of measuring popularity, managing and developing processes, and in evaluating investment support status-whether the source of interactions of any election campaign, brand, product, or advertising on social media is due to popular users, passive users, or spam-bot accounts [8][9][10][11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…Another problem is that bot and fake profiles are used to increase the number of followers [9]. The number of viewers of a social media account and its size, in terms of number of followers or friends, is a good measure of its user popularity.…”

Section: Related Workmentioning

confidence: 99%

Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

İş

Tuncer

2019

Applied Sciences

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This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter. Behavioral analyses of Twitter users were performed by using their profile structures and interaction types, and Twitter users were classified according to their effect size values by determining their asset values. User profiles were classified into three different categories, namely popular-active, observer-passive, and spam-bot-malicious by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms. For classification, the study used the basic characteristics of users, such as density, centralization, and diameter, as well as suggested time series such as the simple moving average and cumulative moving average. The highest accuracy was obtained by the K-NN algorithm. The results obtained with K-NN for all classes were higher than the F1-Score values obtained for the other algorithms. According to the results obtained, classification accuracy values were found to reach a maximum of 96.81% and a minimum of 92.33%. Our classification results showed that the proposed method was satisfactory for popular-active, observer-passive, and spam-bot-malicious account separation.

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“…The point of detection of phony account is removing the most essential substance at the social network account holders. [1]. The proposed strategy has a structure like three developed based on demonstrate nonstructural attributes with logical relations among various real time genuine OSN account holder's Reviews Tweets Comments analysis (RTC) sections of the social networks.…”

Section: Introductionmentioning

confidence: 99%

A fusion fuzzy model for detecting phony accounts in social networks

Rao

Gyani

Narsimha

2018

IJET

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In online social network’s phony account detection is one of the major task among the ability of genuine user from forged user account. The fundamental objective of detection of phony account framework is to detect fake account and removal technique in Social network user sites. This work concentrates on detection of phony account in which it depends on normal basis framework, transformative Algorithms and fuzzy technique. Initially, the most essential attributes including personal attributes, comparability techniques and various real user review, tweets, or comments are extricated. A direct blend of these attributes demonstrates the significance of each reviews tweets comments etc. To compute closeness measure, a consolidated strategy in view of artiﬁcial honey bee state Algorithm and fuzzy technique are utilized. Second approach is proposed to alter the best weights of the normal user attributes utilizing the social network activities/transaction and inherited Algorithm. Finally, a normal rank rationale framework is utilized to calculate the ﬁnal scoring of normal user activities. The decision making of proposed approach to find phony account are variation with existing techniques user behavioral analysis using data sets and machine learning techniques such as crowdflower_sample and genuine_accounts_sample dataset of facebook and Twitter. The outcomes demonstrate that proposed strategy overcomes the previously mentioned strategies.

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Malicious accounts: Dark of the social networks

Cited by 137 publications

References 96 publications

Detection of malicious social bots: A survey and a refined taxonomy

Detection of malicious social bots: A survey and a refined taxonomy

Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

A fusion fuzzy model for detecting phony accounts in social networks

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