In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time.

Concone F., Lo Re G., Morana M., Das S.K. (2022). SpADe: Multi-Stage Spam Account Detection for Online Social Networks. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 1-16 [10.1109/TDSC.2022.3198830].

SpADe: Multi-Stage Spam Account Detection for Online Social Networks

Concone F.
;
Lo Re G.;Morana M.;
2022

Abstract

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
https://ieeexplore.ieee.org/document/9857601
Concone F., Lo Re G., Morana M., Das S.K. (2022). SpADe: Multi-Stage Spam Account Detection for Online Social Networks. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 1-16 [10.1109/TDSC.2022.3198830].
File in questo prodotto:
File Dimensione Formato  
TDSC_2022.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione Editoriale
Dimensione 7.22 MB
Formato Adobe PDF
7.22 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/567942
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact