Modern cybersecurity challenges span multiple layers, from human behavior and identity management to network communication and device security. This paper proposes a unified multi-layered security framework that integrates human-centric, identity-centric, and communication-centric defenses into a coherent architecture. Drawing on insights from diverse domains (industrial control systems, IoT, healthcare, blockchain, and quantum communications), we identify common defense-in-depth principles and interdependencies across layers. The study highlights the persistent gaps in current research, which often focuses on isolated layers or domain-specific models, and addresses these gaps by synthesizing a cross-domain framework. We develop a mixed-method methodology to compare and integrate multi-layer security mechanisms, and we implement a proof-of-concept risk assessment engine to evaluate the framework’s effectiveness. Preliminary results from this implementation demonstrate that combining layers yields significantly improved detection performance and resilience compared to single-layer baselines. The framework’s contributions include a comprehensive literature-driven model, an operational validation in a simulated environment, and guidelines for deploying multi-layer defenses in complex, interconnected infrastructures. Empirical findings confirm that an integrated multi-layer approach can adapt to varied threat scenarios and reduce vulnerabilities, underscoring the value of coordinated controls across technical and human factors. The proposed framework lays a foundation for future work on scalable, cross-layer cybersecurity architectures that better protect contemporary cyber–physical systems.

Content Management Systems (CMS) have fundamentally transformed the digital landscape, powering a substantial majority of the modern web. However, the ubiquity of platforms such as WordPress, Joomla, and Drupal has made them primary targets for cybercriminals. The central security weakness lies not within the core software but within the complex ecosystem of third-party extensions and themes, which account for the vast majority of reported vulnerabilities. This article presents a comprehensive analysis of the current CMS security landscape, synthesizing empirical data on the prevalence of outdated components and the efficacy of existing scanning tools. Based on a critical review of recent literature and an analysis of common attack vectors, the study identifies the fundamental limitations of traditional, reactive security paradigms that rely on signature-based detection. To address the critical gap in detecting zero-day threats, this research proposes and implements a proactive vulnerability detection system—SentinelCMS—combining static Taint Analysis with a Bidirectional LSTM neural network classifier. The system was validated on an augmented dataset of 600 PHP code samples using 5-fold cross-validation. An experimental proof of concept validates that static analysis of plugin source code can extract semantic features to accurately classify vulnerabilities into specific categories (SQL Injection, Cross-Site Scripting, Remote Code Execution) with an overall accuracy of 93%. The implementation of this system aims to shift the cybersecurity paradigm from incident response to threat prevention, thereby significantly enhancing the resilience of web resources.

Background: WordPress plugins account for the large majority of disclosed CMS vulnerabilities, and learning-based detectors report high accuracy on synthetic corpora and random splits. Methods: We build a benchmark from 1757 real plugin CVEs (6666 indexed identifiers, 1281 plugins), yielding 30,860 labeled PHP functions in two negative-sampling variants. An audit of the released artifacts found exact normalized-code duplicates crossing test folds (166 groups/1086 records in Variant A; 151/676 in Variant B) and identical code carrying contradictory labels (730 and 456 groups). After removing conflicting groups, merging exact duplicates and collapsing token- and AST-level clone families, the corpora contain 15,294 (Variant A) and 24,917 (Variant B) records with zero shared hashes, clone families or plugins across folds. Ten detectors are compared under plugin-grouped five-fold cross-validation on complete held-out folds: a static taint analyzer, TF-IDF Random Forest and SVM, Bi-LSTM, unidirectional LSTM, a taint-augmented neural model, a nested out-of-fold stacking ensemble, and three baselines (majority class, a source/sink presence rule, and a single-feature function-length classifier). Results: The best macro-F1 is 0.563 (Variant A, binary, SVM) and 0.601 (Variant B, binary, nested stacking). Neural models do not lead in any of the six configurations, and the single-feature length baseline matches or exceeds the TF-IDF Random Forest in four of six. The source/sink rule attains an MCC close to zero, and the taint analyzer misses about 98% of vulnerabilities. Manual review of 400 functions finds 48.8% of cleaned positive labels to be genuine vulnerabilities. Conclusions: Under duplicate-controlled evaluation, the differences between detector families are small compared with the variation attributable to label quality, which appears to be the more binding constraint. All data, folds, predictions and fingerprinted result files are released.