Sensory Data-driven Modeling of Adversaries in Mobile Crowdsensing Platforms

The advent of Mobile crowdsensing (MCS) facilitates the adoption of ubiquitous sensing solutions in smart environments. Despite its benefits, MCS calls for proper security and trust solutions. Various threatening attacks, such as injection attacks, can compromise both the veracity and integrity of crowdsensed data. This work leverages adversarial machine learning to introduce a smart injection attacker model (SINAM) that may be used in the design of security solutions against injection attacks in MCS. SINAM has been validated during an authentic MCS campaign. Unlike most random data injection models, SINAM monitors data traffic in an online-learning manner, successfully injecting malicious data across multiple victims with near-perfect accuracy rates of 99%. SINAM uses accomplices within the sensing campaign to predict accurate injections based on both behavioral analysis and context similarities.