## 9 Discussion
### Future Research Directions
This ultimately results in a reduction in latency and a significant reduction in the possibility of data breaches occurring during transmission. Additionally, TinyML can be designed to continually monitor the sensor data and network activity inside the car for any anomalies [197]. TinyML has the ability to learn to recognize potentially suspicious patterns that may indicate a cyber assault. These patterns may include an effort to modify sensor readings or control messages. TinyML can learn to recognize these patterns via the use of powerful machine learning models. When an anomaly is discovered, it is possible for it to immediately trigger countermeasures. These countermeasures may include disconnecting the compromised component or contacting a central security system. In addition, since TinyML utilizes such a little amount of power, it is able to maintain its operational state even when the vehicle is not in use, which ensures that the user is always protected. Additionally, the predictive capabilities of TinyML models could be applied to anticipate developing attack vectors, allowing for the creation of proactive security measures. This can be accomplished via the utilization of TinyML's predictive analytics. TinyML's incorporation into autonomous vehicles provides, in essence, an effective method for enhancing cyber security, shortening the reaction times to security events, and ensuring the integrity and resilience of vehicular systems in the face of an ever-evolving environment of cyber threats. 2. **Integration of reinforcement learning, Markov decision processes, and intelligent Rainbow DQN agents in AV cyber security:** The combined power of reinforcement learning (RL), Markov decision processes (MDPs), and intelligent Rainbow DQN agents can serve as a formidable arsenal to enhance the cyber security of autonomous vehicles (AVs). Reinforcement learning, another type of machine learning, can be applied to analyze the patterns and potential vulnerabilities in AV systems by continuously learning from the environment and optimizing responses under different circumstances. Within the framework of Markov decision processes, RL algorithms can be implemented. MDPs evaluate the present state of the system, available actions, transition probabilities, and rewards, enabling RL algorithms to make decisions that maximize some concept of cumulative reward. This is particularly beneficial in the context of cyber security, as the system can learn to make judgments that reduce or prevent incidents related to cyber security. At this point, Rainbow DQN agents incorporate a number of developments in deep Q networks (DQNs) and reinforcement learning. These agents are proficient at managing high-dimensional state spaces, which are typical of AV systems with numerous sensors and intricate networking. This knowledge can be efficiently processed by Rainbow DQN agents, enabling them to make rapid choices based on knowledge. Regarding cyber security, these intelligent agents may be deployed to continuously monitor the in-vehicle networks and data flows. They can detect anomalous patterns or intrusions that may be indicative of cyber attacks or system compromises by gaining knowledge from the data. In addition, through reinforcement learning and its decision-making process, these agents have the ability to predict the likely evolution of an attack, enabling preventative steps to be taken before the attack can have a negative impact. As an example, if an attack pattern is identified, the agent can decide to isolate portions of the vehicle's network, restrict communication with the suspected compromised components, or activate other defense mechanisms. This proactive and learning-based approach facilitated by the synergy of RL, MDPs, and Rainbow DQN agents can substantially increase the resilience and security of autonomous vehicles against complicated and evolving cyber threats. In conclusion, these outlined future research directions underscore the need for a holistic, comprehensive approach to cyber security within AVs.