DocumentCode :
3540375
Title :
Leveraging online social networks for a real-time malware alerting system
Author :
Al-Qasem, Isra ; Al-Qasem, Sumaya ; Al-Hammouri, Ahmad T.
Author_Institution :
Jordan Univ. of Sci. & Technol., Irbid, Jordan
fYear :
2013
fDate :
21-24 Oct. 2013
Firstpage :
272
Lastpage :
275
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;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Local Computer Networks (LCN), 2013 IEEE 38th Conference on
Conference_Location :
Sydney, NSW
ISSN :
0742-1303
Print_ISBN :
978-1-4799-0536-2
Type :
conf
DOI :
10.1109/LCN.2013.6761247
Filename :
6761247
Link To Document :
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