%0 Conference Proceedings
%T Detecting Mobile Spam Botnets Using Artificial immune Systems
%+ University of Pretoria [South Africa]
%+ Absa Capital [Johannesburg]
%A Vural, Ickin
%A Venter, Hein
%Z Part 3: FRAUD AND MALWARE INVESTIGATIONS
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 7th Digital Forensics (DF)
%C Orlando, FL, United States
%Y Gilbert Peterson
%Y Sujeet Shenoi
%I Springer
%3 Advances in Digital Forensics VII
%V AICT-361
%P 183-192
%8 2011-01-31
%D 2011
%R 10.1007/978-3-642-24212-0_14
%K Botnets
%K mobile devices
%K malware
%K artificial immune systems
%Z Computer Science [cs]Conference papers
%X Malicious software infects large numbers of computers around the world. Once compromised, the computers become part of a botnet and take part in many forms of criminal activity, including the sending of unsolicited commercial email or spam. As mobile devices become tightly integrated with the Internet, associated threats such as botnets have begun to migrate onto the devices. This paper describes a technique based on artificial immune systems to detect botnet spamming programs on Android phones. Experimental results demonstrate that the botnet detection technique accurately identifies spam. The implementation of this technique could reduce the attractiveness of mobile phones as a platform for spammers.
%G English
%Z TC 11
%Z WG 11.9
%2 https://inria.hal.science/hal-01569560/document
%2 https://inria.hal.science/hal-01569560/file/978-3-642-24212-0_14_Chapter.pdf
%L hal-01569560
%U https://inria.hal.science/hal-01569560
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-DF
%~ IFIP-WG11-9
%~ IFIP-AICT-361