Digital Twin-Based Cyber Risk Forecasting in Smart Cities

Abstract

Cyberwarfare has evolved into a sophisticated domain where attackers exploit advanced persistent threats, zero-day exploits, and social engineering to compromise critical systems. Traditional reactive defenses—firewalls, signature-based intrusion detection systems, and static honeypots—are increasingly inadequate against adaptive adversaries who reconnoiter, probe, and pivot within target networks. AI-based deception techniques offer a proactive layer of defense by dynamically generating decoys, obfuscating real assets, and engaging adversaries in controlled environments, thereby gathering actionable threat intelligence and disrupting attack chains. This manuscript presents a comprehensive framework for the design, implementation, and evaluation of an AI-driven deception platform tailored for cyberwarfare defense. We begin by delineating the theoretical underpinnings of deception in cyber defense and surveying existing approaches to honeypots, honeytokens, and dynamic decoys. Building on this foundation, we describe our methodological approach: a modular system architecture comprising a Decoy Generator that fabricates realistic service instances; a Behavior Analyzer that employs machine learning models to classify traffic and predict adversary intent; and an Orchestration Engine that adapts deception strategies in real time. We deploy the platform within a simulated enterprise network and conduct three representative attack scenarios—reconnaissance, credential brute-forcing, and lateral movement—under both static and AI-based configurations. Our results demonstrate a significant increase in detection rates (from 72.5% to 94.2%), extended adversary engagement times (by 133%), and richer intelligence collection, all achieved with acceptable computational overhead. We conclude with a critical analysis of operational considerations, including model maintenance, integration with existing security infrastructures, and adversary-aware countermeasures, and outline future research directions such as federated learning for collaborative deception and advanced generative models for decoy authenticity.