AI-Driven Attack Modeling and Defense Strategies in Mobile Crowdsensing: A Special Case Study on Fake Tasks

Technological advancements in information and communication have brought up ubiquitous sensing as a key concept to obtain valuable crowd data. Mobile crowdsensing (MCS) has emerged as an essential data collection opportunity through a wide variety of smart device sensors. This technology has increased its popularity due to low data collection and sensing cost through open platforms where smart device users can reach out to the offers coming from MCS platforms. MCS campaigns aim to ensure that all resources are used for the required amount of time with the desired capacity; hence, trustworthy services can be offered to all stakeholders in a MCS platform. However, it is a big challenge to protect smart device users and MCS platform against cyber-attacks. Therefore, it is vital to implement intelligent attack and defense mechanisms before launching a MCS campaign. Anticipating fake task injections is one of the crucial strategies to be considered through artificial intelligent empowered attack modeling to forecast the impact of the attack on draining computational resources. Self-organizing feature map (SOFM) has been leveraged on fake task injection modeling to increase the impact of the attack strategy through launching fake sensing tasks in locations with the highest impact. Moreover, defense mechanism for legitimacy detection is a paramount factor to mitigate fake task submission effects on the users and platforms. Machine learning strategy should monitor the task generation process to eliminate malicious activities in the campaign; thus trustworthy environment for data collection can be achieved for task submission and sensing via smart devices. This chapter presents the state of the art of MCS security from the anticipatory and defensive perspectives through AI-enabled schemes. Furthermore, it unveils the open issues and challenges that remain roadblocks against wide adoption of MCS-enabled systems.