A collaborative approach to botnet protection

Publication: Research - peer-reviewConference article – Annual report year: 2012

View graph of relations

Botnets are collections of compromised computers which have come under the control of a malicious person or organisation via malicious software stored on the computers, and which can then be used to interfere with, misuse, or deny access to a wide range of Internet-based services. With the current trend towards increasing use of the Internet to support activities related to banking, commerce, healthcare and public administration, it is vital to be able to detect and neutralise botnets, so that these activities can continue unhindered. In this paper we present an overview of existing botnet detection techniques and argue why a new, composite detection approach is needed to provide efficient and effective neutralisation of botnets. This approach should combine existing detection efforts into a collaborative botnet protection framework that receives input from a range of different sources, such as packet sniffers, on-access anti-virus software and behavioural analysis of network traffic, computer sub-systems and application programs. Finally, we introduce ContraBot, a collaborative botnet detection framework which combines approaches that analyse network traffic to identify patterns of botnet activity with approaches that analyse software to detect items which are capable of behaving maliciously. © 2012 IFIP International Federation for Information Processing.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Publication date2012
Volume7465
Pages624-638
ISSN0302-9743
DOIs
StatePublished

Conference

ConferenceInternational Cross-Domain Conference and Workshop on Availability, Reliability, and Security (CD-ARES 2012)
CountryCzech Republic
CityPrague
Period20/08/1224/08/12
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Health care, Internet
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

ID: 57602151