As the digital landscape becomes increasingly complex, traditional cybersecurity measures are struggling to keep pace with the growing sophistication of cyber threats. This escalating challenge calls for new, more robust solutions. In this context, Quantum Computing emerges as a powerful tool that can change our approach to network security. Our research addresses this by introducing QuantumNetSec, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QuantumNetSec employs Quantum Machine Learning (QML) personalized methodologies to analyze network patterns and detect malicious activities. Through detailed experimentation with publicly shared datasets, QuantumNetSec demonstrated superior performance in both binary and multiclass classification tasks. Our findings highlight the significant potential of quantum-enhanced cybersecurity solutions, showcasing QuantumNetSec’s ability to accurately detect a wide range of cyber threats, paving the way for more resilient and effective intrusion detection systems in the age of quantum utility.

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.