A network for collaborative detection of intrusions in smart cities using blockchain technology

Abstract

The field of cybersecurity has undergone significant transformation with the integration of machine learning (ML) and artificial
intelligence (AI) techniques into intrusion detection systems (IDS). This research article presents a comprehensive survey spanning
the past five years, exploring the symbiotic relationship between ML, AI, and intrusion detection. The survey traverses seminal studies,
methodologies, and results, shedding light on an evolving landscape characterized by innovation and advancement. The classification
report’s key metrics—precision, recall, F1-score, and support. High precision values point to accurate positive predictions, while recall
values showcase the model’s ability to capture true instances. The F1-score signifies the equilibrium between precision and recall. Thesemetrics collectively underscore the model’s proficiency in identifying and differentiating intrusion classes, reinforcing its real-worldapplicability. In conclusion, this research article presents a holistic view of ML and AI integration with intrusion detection, offeringinsights into innovative contributions and their implications for cybersecurity. While highlighting existing research gaps, the articleunderscores the potential of AI-driven intrusion detection systems and advocates for ongoing advancements to fortify digital securityagainst emerging threats.