The growing complexity of cyberattacks in future-generation network infrastructures requires advanced intrusion detection systems with adaptive learning and response functions to respond to dynamic attack vectors. Various attacks can compromise the reliability of vehicular applications and services, leading to incorrect decision-making by vehicles and infrastructure, and potentially causing accidents or traffic congestion. For example, a malicious node could inject false information about traffic conditions, leading vehicles to take unnecessary detours or make unsafe maneuvers. Thus, an attacker can easily inject any leading system faults. This chapter offers a comprehensive comparative analysis of artificial intelligence(AI) techniques applied to network intrusion detection, comparing the performance of conventional machine learning techniques with deep learning models. By performing extensive experimentation across four well-known benchmark datasets—Canadian Institute for Cybersecurity Intrusion Detection System 2018 (CICIDS-2018), Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS-2017), Aegean Wi-Fi Intrusion Dataset (AWID), and Network Security Laboratory Knowledge Discovery Dataset (NSL-KDD)—we comparatively evaluate Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR) classifiers against Long Short-Term Memory (LSTM) networks. Empirical evaluation is conducted across various network traffic scenarios, including distributed denial-of-service (DDoS) attacks, brute-force intrusions, and wireless network vulnerabilities. The results affirm the superior performance of LSTM networks, which achieved outstanding accuracy levels of 99.56, 98.87, 99.69, and 98.77% on their respective datasets, significantly outperforming traditional machine learning models. The performance of the LSTM architecture for recognizing temporal dependencies and sequential patterns in network traffic data defines its paradigmatic superiority in identifying sophisticated cyber attacks. These findings and analysis indicate the efficiency of deep learning methods to boost the security of cyber-physical systems and deal with future security issues, specifically in TCP-based networks as well as centralized and decentralized environments.