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

Leandro Antonio Pazmiño Ortiz (), Ivonne Fernanda Maldonado Soliz and Vanessa Katherine Guevara Balarezo
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Leandro Antonio Pazmiño Ortiz: Escuela de Formación de Tecnólogos, Escuela Politécnica Nacional, Quito 170525, Ecuador
Ivonne Fernanda Maldonado Soliz: Escuela de Formación de Tecnólogos, Escuela Politécnica Nacional, Quito 170525, Ecuador
Vanessa Katherine Guevara Balarezo: Escuela de Formación de Tecnólogos, Escuela Politécnica Nacional, Quito 170525, Ecuador

Future Internet, 2025, vol. 17, issue 8, 1-41

Abstract: 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. Employing a Holistic and Integrative Review methodology, this paper dissects the intricate, adaptive ecosystem of MaaS-driven cookie theft. We systematically characterize the co-evolving arms race between offensive and defensive strategies (2020–2025), revealing a critical strategic asymmetry where attackers optimize for speed and low cost, while effective defenses demand significant resources. To shift security from a reactive to an anticipatory posture, a multi-dimensional predictive framework is not only proposed but is also detailed as a formalized, testable algorithm, integrating technical, economic, and behavioral indicators to forecast emerging threat trajectories. Our findings conclude that long-term security hinges on disrupting the underlying cybercriminal economic model; we therefore reframe proactive countermeasures like Zero-Trust principles and ephemeral tokens as economic weapons designed to devalue the stolen asset. Finally, the paper provides a prioritized, multi-year research roadmap and a practical decision-tree framework to guide the implementation of these advanced, collaborative cybersecurity strategies to counter this pervasive and evolving threat.

Keywords: cookie theft; Malware-as-a-Service; cybercrime economics; adaptive ecosystem; threat forecasting; cyber arms race; economic disruption; Zero-Trust Architecture (ZTA); dynamic defense; session hijacking; infostealer malware; adversarial machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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