# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 2 Background and Related works
### Related Works
The IDS demonstrates a detection accuracy exceeding 90% for spoofing attacks. The authorsintroduce a new metric named Position Verification using Relative Speed (PVRS), which significantly influences the classification results. PVRS compares the observed distance between communicating nodes, as captured by On-Board Units (OBU), with the estimated distance obtained through the calculation of relative speed values using exchanged signals in the Physical (PHY) layer. The proposed IDS presents an effective approach to detect spoofing attacks in VANETs. [PERSON] et al. (2018) emphasizes the implementation of Software-Defined Network (SDN) in IoT networks to provide a more cost-effective solution compared to traditional hardware components. The paper delves into the application of IDS within SDN-based IoT networks, presenting a comprehensive analysis of various studies and conducting comparisons. The primary objective of this survey is to introduce innovative research avenues for SDN-based IoT networks. Additionally, the paper explores the integration of blockchain technology as a means to enhance security in SDN-based IoT networks. [PERSON] et al. (2018) examine recent advancements in electric vehicles (EVs) and emerging technologies for e-mobility by 2030. It highlights the benefits of integrating autonomous features in EVs to improve safety and fuel efficiency. The research addresses research gaps and proposes solutions for intelligent vehicles, emphasizing the importance of real-world data and exploring various EV-related fields. In this article (2018), a novel approach is introduced for the early and accurate detection of botnet attacks, utilizing common network traffic patterns and temporal features. The study evaluates and compares different ML algorithms, including DT, probabilistic neural network, sequential minimal optimization, and Adaboost classifiers, against existing research. The findings demonstrate the effectiveness of the proposed approach, achieving an impressive true positive rate of 99.7%, and emphasize the significance of incorporating temporal features to enhance botnet detection efficiency. This article (2018) introduces a unique simulation technique to create the VANET network distributed DOS dataset, specifically designed for VANET. The dataset captures essential aspects such as network architecture, traffic density, attack intensity, and node mobility, offering comprehensive information on distributed denial of service attacks. Comparisons with existing studies confirm the novelty of the dataset, while the evaluation of various ML models demonstrates their high accuracy rates exceeding 99.5%, except for the SVM achieving 97.3%. After examining the recent work outlined in Table 1, we have identified numerous challenges and limitations. VANETs, characterized by high vehicle mobility, shared network medium, and the absence of centralized security devices like firewalls and authentication servers, are highly susceptible to attacks on wired networks. Managing the growing volume of vehicular traffic in urban environments poses a significant challenge for IDS. Additionally, VANET networks and their critical infrastructure interface face ongoing security challenges in terms of rapidly transmitting, receiving, archiving, and retrieving information across the network. To address these concerns, we propose an IDS based on an RF classifier, aiming to enhance precision and accuracy. Our model's robustness is demonstrated by comparing it with various ML--DT, KNN, LR, and SVM techniques--and testing it on different datasets--NSL-KDD, TON-IOT, and CICID2017.