Detection of compromised accounts for online social networks based on a supervised analytical hierarchy process

Wang, Xiujuan; Tang, Haoyang; Zheng, Kan; Tao, Yuanrui

doi:10.1049/iet-ifs.2018.5286

Cited by 6 publications

(2 citation statements)

References 29 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another work that makes use of behavioral profiles for is Velayudhan and Bhanu (2020), where frequency of benign and anomalous tweets are used for compromised account detection. Furthermore, Wang et al (2020) models a user's expression habits by utilizing a supervised analytical hierarchy process for feature selection. A major drawback of these methods is that they rely on the integration of the accounts into the social network or use features that are only accessible by the social network provider.…”

Section: Related Workmentioning

confidence: 99%

Textual Analysis and Timely Detection of Suspended Social Media Accounts

Seyler

Tan

et al. 2021

ICWSM

View full text Add to dashboard Cite

Suspended accounts are high-risk accounts that violate the rules of a social network. These accounts contain spam, offensive and explicit language, among others, and are incredibly variable in terms of textual content. In this work, we perform a detailed linguistic and statistical analysis into the textual information of suspended accounts and show how insights from our study significantly improve a deep-learning-based detection framework. Moreover, we investigate the utility of advanced topic modeling for the automatic creation of word lists that can discriminate suspended from regular accounts. Since early detection of these high-risk accounts is crucial, we evaluate multiple state-of-the-art classification models along the temporal dimension by measuring the minimum amount of textual signal needed to perform reliable predictions. Further, we show that the best performing models are able to detect suspended accounts earlier than the social media platform.

show abstract

Section: Related Workmentioning

confidence: 99%

Textual Analysis and Timely Detection of Suspended Social Media Accounts

Seyler

Tan

et al. 2021

ICWSM

View full text Add to dashboard Cite

show abstract

“…e accuracy of the test results depended largely on the established behavioral profile and threshold value of selection. Tang et al proposed the supervised analytic hierarchy process (SAHP) for abnormal user detection [41]. In the process of abnormal user detection, different characteristics often reflect different degrees of user abnormality.…”

Section: Contrast Experimentmentioning

confidence: 99%

Detecting Abnormal Social Network Accounts with Hurst of Interest Distribution

Wang

Sui

Tao

et al. 2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

With the rapid development of the Internet since the beginning of the 21st century, social networks have provided a significant amount of convenience for work, study, and entertainment. Specifically, because of the irreplaceable superiority of social platforms in disseminating information, criminals have thus updated the main methods of social engineering attacks. Detecting abnormal accounts on social networks in a timely manner can effectively prevent the occurrence of malicious Internet events. Different from previous research work, in this work, a method of anomaly detection called Hurst of Interest Distribution is proposed based on the stability of user interest quantifiable from the content of users’ tweets, so as to detect abnormal accounts. In detail, the Latent Dirichlet Allocation model is adopted to classify blog content on Twitter into topics to calculate and obtain the topic distribution of tweets sent by a single user within a period of time. Then, the stability degree of the user’s tweet topic preference is calculated according to the Hurst index to determine whether the account is compromised. Through experiments, the Hurst indexes of normal and abnormal accounts are found to be significantly different, and the detection rate of abnormal accounts using the proposed method can reach up to 97.93%.

show abstract

Anomaly detection and optimization using scalable and flexible network data characterization

Sethi

Behera

2021

Global Transitions Proceedings

View full text Add to dashboard Cite

Detection of compromised accounts for online social networks based on a supervised analytical hierarchy process

Cited by 6 publications

References 29 publications

Textual Analysis and Timely Detection of Suspended Social Media Accounts

Textual Analysis and Timely Detection of Suspended Social Media Accounts

Detecting Abnormal Social Network Accounts with Hurst of Interest Distribution

Anomaly detection and optimization using scalable and flexible network data characterization

Contact Info

Product

Resources

About