The Adaptive Ecosystem of MaaS-Driven Cookie Theft: Dynamics, Anticipatory Analysis Concepts, and Proactive Defenses

The industrialization of cybercrime, principally through Malware-as-a-Service (MaaS), has elevated HTTP cookie theft to a critical cybersecurity challenge, enabling attackers to bypass multi-factor authentication and perpetrate large-scale account takeovers. This review dissects the intricate, adaptive ecosystem of MaaS-driven cookie theft, offering a comprehensive analysis of its operational, economic, and co-evolutionary dimensions. We introduce a novel conceptual model delineating the key actors, interdependencies, and socio-economic feedback loops that sustain this illicit economy. Furthermore, the paper systematically examines the adaptive offensive and defensive strategies employed between 2020 and 2025, critically evaluating their mechanisms, efficacy, and inherent trade-offs. A conceptual multi-dimensional predictive framework is proposed, integrating technical, economic, and behavioral indicators to foster anticipatory insights into emerging threat trajectories. Our findings underscore the imperative for proactive, layered, and anticipatory security architectures, advocating for the adoption of advanced countermeasures such as zero-trust principles, ephemeral token strategies, and dynamic defense policies. Finally, we identify critical research gaps and propose structured recommendations for advancing predictive analytics, disrupting cybercriminal economic incentives, and enhancing collaborative cybersecurity strategies to counter this pervasive and evolving threat.