## 3 Related Work  
In the last few years, researchers have been getting excited about \"ensemble learning\". This approach has been making waves in the world of connected cars, especially when it comes to spotting cyber threats. Looking at what's been published so far, we can see both the hurdles and breakthroughs in keeping these connected vehicles safe using ML. While there has been lots of research on different team-based learning approaches, two studies really stand out from the crowd: one by [PERSON] et al. [35], and the other by [PERSON] et al. [36]. [PERSON] et al. [35] took a deep dive into how ML can help secure the conversations happening between vehicles. They highlighted just how important threat detection systems are for keeping vehicle networks safe. But they also pointed out some real challenges, such as current systems needing serious computing power, and cars are constantly moving around, which makes detecting threats in real-time pretty tricky. They suggested developing lighter-weight detection systems that could run on edge computing (i.e., processing data closer to where it is created rather than sending everything to a central server). Their work really opened eyes to the need for more efficient solutions that balance strong security with the practical limitations of connected cars. Building on this foundation, [PERSON] et al. [36] analyzed various ML frameworks for threat detection in vehicle networks. While they looked at many different types of models, they focused on addressing some key problems: vulnerability to adversarial attacks (where someone deliberately tries to trick the system), increased lag time, and high energy use. They suggested future research should prioritize developing models that can resist these tricky attacks while staying lightweight and energy-efficient, qualities that are essential in the constrained environment of connected vehicles. Their work highlights the growing need for threat detection systems that not only spot problems accurately but also use minimal resources and can adapt to new attack patterns on the fly. The authors advocate for creating open-access datasets and utilizing advanced architectures like memory-augmented networks and Generative Adversarial Networks (GANs) to strengthen model generalization. Together, these studies highlight two major gaps that need filling: we need lightweight models that can work effectively with limited resources, and we need systems that can handle emerging threats without taking a performance hit. [PERSON] et al. [10] examined the use of ML, reinforcement learning, and transformer-based models for securing Controller Area Network (CAN) protocols in IoV. The study stresses computational inefficiency and lack of real-world CAN data as major barriers. Their recommendation is to design energy-efficient DL models and develop standardized, public CAN datasets for more robust IDS evaluation. [PERSON] et al. [11] explored future security enhancements in vehicular IDSs using lightweight DL models and adaptive detection strategies. Their key limitation lies in the integration of real-time mechanisms into constrained vehicular environments, prompting the need for adaptive, scalable IDS frameworks. [PERSON] et al. [37] focused on IDS designs for in-vehicle networks, emphasizing hybrid and flow-based approaches tailored to CAN traffic. Their study reveals that current IDSs often fall short in detecting novel attacks and are hindered by the lack of standardized benchmarks. They call for future research on more effective lightweight IDSs with standardized evaluation protocols for CAN-based vehicular networks. Table 3 shows a comparison of our survey with respect to previous similar work. Related surveys highlighted noteworthy successes and obstacles in implementing ML and DL methodologies to enhance intrusion detection and security within automotive networks and IoV.