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May 10, 2026INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCHOpen Access

CyberGuardX (Cerberus-AI CyberShield): An Intelligent Threat Detection with Explainable AI and Real-Time Threat Response

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Authors

RSRaj SinghMTManjesh TiwariUTUtkarsh Tiwari

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Overview

Randomized trial demonstrates effective malware detection in enterprise security, highlighting AI advancements.

Key Points

  • This paper aims to improve malware detection capabilities using explainable AI and real-time response mechanisms.
  • Developed a multi-modal malware detection system named CyberGuardX using static analysis and machine learning.
  • Utilized Random Forest classifiers with deep static features from PE files, PDFs, and documents for training.
  • Integrated VirusTotal API for threat intelligence and implemented real-time file system monitoring.
  • Achieved 99.9% accuracy in malware detection using the CyberGuardX system.
  • Demonstrated response times of less than 2.3 seconds for analysis processes.
  • Showed significantly higher detection percentages compared to traditional signature-based systems.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6a0021cdc8f74e3340f9cadchttps://doi.org/10.56975/ijedr.v14i1.304397
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1DeepGuardXAI: Human-Centric and Explainable AI for Enhancement in Cybersecurity Decision-Making2026
  2. 2A Real-Time Threat Intelligence System for Comprehensive Cyber Attack Detection and Mitigation Using Explainable AI2026
  3. 3HybridML CyberShield for explainable proactive intrusion detection in enterprise and IoT networks2026
  4. 4CyberAI - Agentic malware analysis2026
  5. 5Towards Smarter Cyber Threat Detection and Response with Explainable AI2025