Comparative Study of Misbehavior Detection System for Classifying misbehaviors on VANET

Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges

“…In this direction, [35] uses six different supervised techniques along with plausibility checks to come up with a multiclass classification approach on the VeReMi dataset. Using the same dataset, [36] also discusses the use of different supervised techniques considering various feature extraction methods. Additionally, [37] uses an optimized version of Random Forest (RF), which is compared with other techniques such as K-Nearest Neighbors (KNN) and Decision Trees (DT).…”

One Versus All Binary Tree Method to Classify Misbehaviors in Imbalanced VeReMi Dataset

“…The database includes a number of errors caused by faulty On Board Units or vehicle sensors that result in inaccurate results for position, acceleration, velocity, and heading, as well as a variety of cyber-attacks when the vehicle sends an incorrect message. Our earlier study contains more information [7]. For easier reading, an open-source online data repository maintained by Mendeley Data [33].…”

“…Our earlier study contains more information [7]. For easier reading, an open-source online data repository maintained by Mendeley Data [33].…”

“…As will be shown in (7), when the database is unbalanced, the minority classes are prioritized as the "One" class at specific levels and are chosen as the "One" class at each level of the OVA binary tree. This may decrease the possibility of false negatives and increase binary classifier's accuracy for minority Every node of a binary-tree is made up of classes and patterns that set details.…”

“…Misbehavior may manifest itself in a number of ways, such as careless driving, moving violations, illegal access, forgery of data, or even dangerous assaults. These illegitimate or dishonest behaviors have the potential to affect VANET's regular functioning, affect the security of vehicles and passengers, and damage the network's reputation for efficiency [7]. In the context of challenging issues with categorization and when dealing with an unbalanced distribution of instances of misbehavior across various categories, it might be difficult to identify and classify misbehavior in vehicular communications.…”