Missing Value Treatments for Machine Learning-Based Misbehavior Detection Systems: Survey, Evaluation, and Challenges

Misbehavior detection systems (MDS) play a crucial role in vehicular ad hoc networks (VANETs) to guarantee their secure operation. Most recent studies focus on applying machine learning methods to detect misbehavior messages. However, these studies are mainly proposed in the complete VANETs datasets. The MDS with incomplete messages (IMDS) is an inevitable issue that was rarely discussed in previous studies. This survey paper aims to explore the performance of missing value treatments in IMDS. It comprehensively introduces the current missing value treatments as well as the data amputation method from previous studies. Furthermore, this survey simulates the incomplete environments for VANETs datasets and conducts experiments over simulated incomplete datasets with various performance metrics. The experimental results are analyzed and discussed to indicate the best match of missing value treatments for IMDS. Finally, the potential challenges and promising future research directions are also highlighted in this paper.