Detection Of Screen Shadowing-based Visual Data Exfiltration Attacks In Vnc Systems

• Author(s):

  Fejisha Dev E B | Suba A

• Keywords:

  VNC Security, Screen Shadowing, Visual Data Exfiltration, Anomaly Detection, Network Traffic Analysis.

• Abstract:

  With The Increasing Adoption Of Remote Desktop Technologies In Enterprise Environments, The Security Of Visual Network Computing (VNC) Systems Has Become A Critical Concern. Screen Shadowing Attacks Represent A Significant Threat Vector Where Malicious Actors Silently Capture Sensitive Visual Data Displayed On Remote Desktops, Including Passwords, Financial Information, And Confidential Documents. This Paper Proposes The Design And Implementation Of A Real-time Detection System For Identifying Screen Shadowing-based Visual Data Exfiltration Attacks In VNC Environments. The Proposed System Incorporates A Virtual Laboratory Environment Consisting Of Ubuntu-based VNC Server And Kali Linux-based Attacker Systems Connected Through An Isolated Internal Network. Network Traffic Analysis Is Performed Using Wireshark And Tshark Tools To Establish Baseline Traffic Patterns During Normal VNC Usage. Attack Simulations Including Rapid Screen Capture And High-quality Stream Extraction Are Conducted To Generate Attack Traffic Signatures. A Python-based Detection Engine Utilizing Threshold-based Anomaly Detection And Machine Learning Algorithms, Specifically Isolation Forest, Is Implemented To Identify Deviations From Normal Traffic Patterns. The System Provides Real-time Alerting Capabilities And Automated Evidence Capture For Forensic Analysis. Experimental Results Demonstrate That The Proposed System Achieves High Detection Accuracy With Minimal False Positives, Effectively Identifying Screen Shadowing Attacks Through Bandwidth Analysis, Packet Rate Monitoring, And Statistical Pattern Recognition. The Proposed Architecture Provides Organizations With An Effective Tool For Protecting Sensitive Visual Data In VNC-enabled Remote Work Environments.

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Other Details

• Paper id:

  IJSARTV12I4104999

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-14

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