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The recent era of pervasive computing has evolved with various applications and has ground-breaking realities in mobile crowdsourcing (MCS). Multiple attempts have been devoted to integrating MCS with IoT-based smart cities where crowdsensing has played a crucial role in the recent past. Despite having potential features, MCS devices lack efficiency when security aspects are concerned. The current security approaches exercised in MCS operations imply limited features and are not intelligent enough to deal with different types of attacks in IoT smart cities. On the other hand, as MCS communications involve radio environmental mapping functional blocks from communication, it is an obvious situation that leads to a vulnerable situation of which adversarial modules can take advantage of it. There are different types of active and passive modes of attacks that can degrade the Quality-of-Service (QoS) aspects in IoT-driven smart city operations. This study’s prime aim and the appealing theme is to realize the need for resilient approaches to intelligent intrusion detection in MCS to mitigate different attacks. The study also introduces a theoretical approach of cluster-enabled multi-task (CeMT) based on bio-inspired learning modeling of the genetic approach to identify the maximum possible threats and misbehaving devices in the smart city-based MCS operations. The study also evaluated the model’s performance based on the processing time of identifying malicious events and showed the accuracy of detecting misbehaving working associate (WA) modules.

An Intelligent Approach of Intrusion Detection in Mobile Crowd Sourcing Systems in the Context of IoT Based SMART City