Author :
Al-Qasem, Isra ; Al-Qasem, Sumaya ; Al-Hammouri, Ahmad T.
Abstract :
Online social networks (OSNs), such as Twitter and Facebook, allow users to post relatively short update messages online. Users usually post messages reporting on their activities, or even on their feelings. With OSNs´ users reaching hundreds of millions around the globe, their update messages, although are radically very diverse in topics, represent a fertile environment for mining for useful information. Previous studies exploited OSNs to obtain early alerts about earthquakes, to infer websites and online services availability, and to predict stock market moves. In this paper, we propose to utilize OSNs to alarm against the spread of new malware attacks, such as viruses, worms, or Trojan horses. Currently, network administrators and operators use manual and traditional ways of communication, such as phones and e-mails, to warn one another against such attacks. Instead, we propose an automatic platform that mines Twitter posts to provide real-time alerts of malware propagation.
Keywords :
data mining; invasive software; real-time systems; social networking (online); Facebook; OSN; Trojan horses; Twitter; Web sites; automatic platform; e-mails; information mining; malware attacks; network administrators; online services availability; online social networks; phones; real-time malware alerting system; stock market moves prediction; viruses; worms; Conferences; Electronic mail; Malware; Real-time systems; Twitter; Viruses (medical); Crowd Sourcing; Online Social Networks (OSNs); Real-time Malware Alerting; Twitter; Web Data Mining;