# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 2 Background and Related works
### Related Works  
A number of researchers have proposed detection methods for IDS in VANET networks. [PERSON] et al. [29] combined the RF technique with a bidirectional feature selection process of classification. The KDD-CUP99 was cleaned; duplicate data were eliminated; and then, different techniques of preprocessing were applied, such as normalization and discretization. RF-FSR gave excellent results of 99.90% classification, and RF/BER of 99.88%. [PERSON] [30] proposed an IDS for VANET using a dataset called ToN-IoT. They used several techniques to resolve class imbalance, missing data, and encoding of categorical values. The proposed model showed the following results: LR 60.9% of accuracy, NB 51.3%, DT 87%, RF 87%, Adaboost 43.6%, KNN 97.1%, SVM 75.1%, and XGBoost 98%. In this paper, a ML-based IDS was developed by the authors [31] in order to identify the intrusions in VANETs. To guarantee the security of group leaders, they applied an artificial neural network (ANN), and to detect abusive multipoint relays, they employed SVM. Compared with the preceding ML-based approaches, the findings result indicated that the implemented approach had higher robustness and reliability. [PERSON] et al. [32] provided a model for multi-decisional intelligent detection named CEAP that conforms to one of the most important characteristics of VANETs--that they are highly mobile with a high detection rate and low costs. They used SVM for classifying intelligent vehicles as cooperating or misbehaving. The results obtained by the model based on the density scenario of the network are 99.13% for linear kernel, 99.04% for multilayer perception kernel, 99.13% for quadratic kernel, 99.35% for polynomial kernel, and 99.67% gaussian radial basis function kernel. [PERSON], [PERSON], and [PERSON] [33] have developed an application based on the RF to identify possible intrusions on the network. It is integrated with the post-detection phase by utilizing the benefits of the coresets as well as the clustering algorithms. The proposed method had a maximum accuracy of 96.93%, while the smallest accuracy was obtained with Bayesian-Coresets of 82.4%, CNN 95.14%, and SVM 85.2%. The authors in [34] developed a proposed framework using a VANET system network and a multiple agent system of communication which contributes to smart traffic management, helping to make it efficient. In this study [35], an ML-based method is introduced to classify misbehaviors in VANETs. The authors propose a security framework that utilizes packet manipulation to distinguish between malicious and legitimate nodes. The classification process incorporates features like node speed deviation, RSS, and packet delivery and drop counts. The evaluation includes both binary and multi-class approaches, and experiments are conducted in the NCTUns-5.0 simulator using various scenarios. The results demonstrate the effectiveness of the proposed framework in accurately classifying misbehaviors in VANETs, with Random Forest and J-48 classifiers exhibiting superior performance compared to other methods. A novel ECRDP approach of routing has been put forward in [36]. The authors of [37] proposed a new routing protocol based on clustering. [PERSON] et al. [32], propose CEAP, a multi-decision intelligent detection model tailored for VANETs. The model utilizes cooperative monitoring and SVM learning to achieve higher detection rates with minimal overhead. To address the highly mobile nature of VANETs, CEAP is implemented on top of the VANET QoS-OLSR protocol, which considers vehicle mobility metrics for stable clustering and extended network lifetime. Additionally, a propagation algorithm is introduced to reduce the exchange of redundant information and repeated detection steps. Simulation results demonstrate that CEAP outperforms other detection techniques in terms of accuracy, attack detection rate, false positive rate, and packet delivery ratio in high mobility scenarios. [PERSON] et al. [38] introduce a probabilistic cross-layer Intrusion IDS that incorporates ML techniques.