A Machine Learning and Reinforcement Learning Framework for Secure, Personalized Web Portofolios

Firman Sholehudin

doi:10.65475/bkvbjn35

Authors

Firman Sholehudin Universitas Muhamamdiyah Ponorogo Author

DOI:

https://doi.org/10.65475/bkvbjn35

Keywords:

Web Portfolio, Cybersecurity, Machine Learning, Reinforcement Learning, Recommender System

Abstract

Digital, web-based portfolios have become essential tools for students and job seekers to showcase technical skills. However, most current portfolios are static HTML/CSS sites, vulnerable to cyber attacks and unable to personalize content. This study introduces SecurePortfolio, a conceptual framework combining a machine learning security layer to block malicious traffic and a causal reinforcement learning module to adaptively recommend portfolio content. Simulation results show that the security component mitigates most threat vectors, while the personalization engine improves content relevance compared to static layouts. The framework offers a blueprint for intelligent, secure, and context-aware digital portfolios for graduates entering the workforce.

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