Self Organizing Feature Map for Fake Task Attack Modelling in Mobile Crowdsensing

Abstract

Clogging attacks in mobile crowdsensing (MCS) denote injection of fake sensing tasks into MCS campaigns in order to interfere with service ability and user participation in sensing campaigns. Due to the lack of a realistic location-based and energy-oriented clogging attack model in MCS, this type of attacks have not been well investigated. To this end, for the first time, we introduce a self organizing feature map (SOFM)-based clogging attack model that aims at maximizing the number of affected participants according to the location of attack zones. These zones are identified by clustering 2-D coordinates of all potential participants and finding out aggregation areas of their mobile devices. We evaluate and verify the introduced attack model via simulations by comparing it to an attack model that relies on random mobility of illegitimate tasks over the attack zones. Our simulation results demonstrate that SOFM-based modeling of clogging attacks in MCS results in a significant impact with almost 50% affected participant population, 24% affected recruitment decisions, and up to 28% energy overhead introduced by illegitimate tasks injected to the MCS campaigns.