Modeling Adversarial Adaptation in Web Infrastructure via Multi-Cycle Behavioral Forecasting (MCA_FCM)

Modern web infrastructures are increasingly targeted by automated scanners, scraping bots, and adaptive adversaries that evolve their behavior in response to defensive mechanisms. Traditional security approaches rely heavily on static signatures, permanent blacklists, or payload inspection, which fail to capture adversarial adaptation dynamics over time. This work introduces MCA_FCM (Multi-Cycle Adversarial – Forecast & Correlation Model), a behavioral framework designed to model, quantify, and forecast attacker adaptation using real server-side observations. At the core of MCA_FCM lies the Attacker Adaptation Score (AAS), a bounded and explainable metric that combines reappearance probability, mutation likelihood, and normalized frequency signals extracted from production-grade web logs. We present Melona Sentinel, a real-world implementation of MCA_FCM operating in a controlled production environment. The system demonstrates how adversarial behavior can be forecasted across multiple observation cycles without relying on permanent blocking, deep packet inspection, payload-based signatures, or synthetic assumptions. Experimental results show that adversarial persistence and mutation patterns exhibit measurable structure and predictability. By focusing on behavioral evolution rather than static identifiers, MCA_FCM enables proactive defensive strategies based on anticipation instead of reactive filtering. This publication is part of a continuous research line on application-layer security and behavioral analysis, and complements prior work on ASN-level behavioral modeling and protocol-level instrumentation within the Melona Sentinel laboratory.