Combating Mobile Spam through Botnet Detection using Artificial Immune Systems

Ickin Vural (University of Pretoria, Republic of South Africa)

Hein S. Venter (University of Pretoria, Republic of South Africa)

Abstract: Malicious software (malware) infects large numbers of mobile devices. Once infected these mobile devices may be involved in many kinds of online criminal activity, including identity theft, unsolicited commercial SMS messages, scams and massive coordinated attacks. Until recently, mobile networks have been relatively isolated from the Internet, so there has been little need to protect them against Botnets. Mobile networks are now well integrated with the internet, so threats on the internet, such as Botnets, have started to migrate to mobile networks. This paper studies the potential threat of Botnets based on mobile networks, and proposes the use of computational intelligence techniques to detect Botnets. We then simulate mobile Bot detection by detecting anomalies using an artificial immune system implementation on an Android device.

Keywords: Botnet, artificial immune system, computational intelligence, malware, mobile

Categories: J.0