CyberSentinel: A Deep Learning Framework for Fake Content Threat Detection

Abstract

With the proliferation of digital communication, the cyber threat landscape has evolved drastically, manifesting in sophisticated vectors such as polymorphic malware, convincing deepfake media, and targeted phishing. CyberSentinel is an enterprise-grade, fully automated cybersecurity framework designed to proactively detect and prevent malicious digital content in real-time. Unlike traditional antivirus systems that react post-execution, CyberSentinel operates autonomously using a 5-Tier architecture: Input Monitoring, AI Detection via Cross-Modal Fusion, Threat Intelligence utilizing MITRE ATT&CK mapping, Automated Incident Response, and Enterprise SIEM Integration. The system evaluates five core modalities—text, image, video, file, and URL—through specialized neural networks including DistilBERT for NLP, ResNet50 with Error Level Analysis for image forensics, 3D CNN for temporal video assessment, heuristic malware analysis, and URL reputation scoring. Explainable AI (XAI) via SHAP and GradCAM provides transparent decision-making outputs. Evaluation on a dataset of 5,000 mixed authentic and malicious files demonstrates aggregate detection accuracy exceeding 95%. Continuous online learning with human-in-the-loop feedback boosts zero-day threat detection by 33%. The system executes automated containment protocols under 500 milliseconds, validating its effectiveness as a modern AI-driven zero-day threat prevention mechanism.

Introduction

The text highlights the rapid rise of advanced cyber threats such as polymorphic malware, deepfakes, and phishing attacks, which traditional signature-based security systems struggle to detect—especially zero-day and multi-stage attacks. Existing tools operate in isolation, analyzing only one type of data (e.g., text or files), and lack transparency in AI decision-making, limiting their effectiveness.

To address these challenges, the paper introduces CyberSentinel, an integrated, proactive cybersecurity platform built on a novel 5-tier architecture. The system performs multimodal analysis across text, images, videos, URLs, and executable files using specialized deep learning models, enabling detection of complex, combined attack vectors.

Key features include:

• AI-driven threat detection using models like DistilBERT (for text), ResNet (for images), and 3D CNNs (for video deepfakes)
• Automated threat prevention, such as quarantining malicious files and terminating processes without user intervention
• Explainable AI (XAI) using SHAP and GradCAM to make AI decisions transparent and interpretable
• Online learning capability to continuously adapt to new and evolving threats
• Enterprise integration, including dashboards, MITRE ATT&CK mapping, SIEM logging, and real-time voice alerts

The system architecture consists of five tiers: input monitoring, AI detection, threat intelligence, automated incident response, and enterprise integration. It uses modern technologies such as FastAPI, PyTorch, OpenCV, and Docker for scalability and performance.

Conclusion

This paper presented CyberSentinel, an enterprise-grade multimodal cybersecurity framework that bridges the gap between passive AI threat detection and active endpoint mitigation. By orchestrating five specialized deep neural networks within a responsive 5-Tier architecture, the designed system is capable of neutralizing complex, compound vulnerabilities at their point of entry. The inclusion of comprehensive transparency systems through XAI, robust mitigation tools such as automated quarantining (AES-256 encryption), and offline voice alerts transforms individual endpoints into fully secured autonomous nodes.

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Copyright

Copyright © 2026 Dr. Ambika K, Arshath K, Gokulakannan M, Dhanush M, Mahendran K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.