Locally reconfigurable Self Organizing Feature Map for high impact malicious tasks submission in Mobile Crowdsensing

Abstract

Location-based clogging attacks in a Mobile Crowdsensing (MCS) system occur following upon the submission of fake tasks, and aim to consume the batteries and hardware resources of smart mobile devices such as sensors, memory and processors. Intelligent modeling of fake task submissions is required to enable the development of effective defense mechanisms against location-based clogging attacks with fake task submissions. An intelligent strategy for fake task submission would aim to maximize the impact on the participants of an MCS system. With this in mind, this paper introduces new algorithms exploiting the Self-Organizing Feature Map (SOFM) to identify attack locations where fake sensing tasks submitted to an MCS platform are centered around. The proposed SOFM-based model addresses issues in the previously proposed SOFM-based attack models by proposing two ways of refinement. When compared to the former models, which also use SOFM architectures, simulation results show that up to 139.9% of impact improvement can be modeled under the reconfigurable SOFM architectures.