Malvertisements Detection using urlscan.io, Pulsedive, and SucuriSiteCheck
J. O. Okesola, Afolakemi Simbo Ogunbanwo, Ayoade Owoade, Emmanuel O. Olorunnisola, Kennedy Okokpujie · 2023
As revenue on online advertisements continues to grow, Cybercriminals are mingling with the technology organisations to publish and run unsolicited adverts. Advertising companies actually have means of maximising visits to the advertiser' website but they have no control on the contents obtained from the ads auctioned to other providers such as yahoo and Facebook. Such contents may contain unauthorised scripts that may be rerouted to malicious sites where malware could be installed or malicious codes executed. This study therefore presents and implements an automated malvertisement detection system (MDS) by employing three common online detection tools or Intrusion Detection Systems (IDSs) – Pulsedive, SucuriSiteCheck, and Urlscan.io - to crawl malicious ads from 450 websites and represent the results on the confusion matrix. The malvertisement system is real and functional but less effective. When performance metrics were applied on the results. None of the IDSs outperformed the others in all the measures, suggesting that a single metrics is insufficient to objectively measure the effectiveness of the IDSs. An improved performance is expected when other set of IDSs is used in combination to build the MDS.