MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641101758 A) filed by Madhankumar C; Mr. K. Bharath Reddy - Vignan'S Institute Of Management And Technology For Women; Mrs. A. Srilatha - Vignan'S Institute Of Management And; Mrs. Ch. Navaneetha - Vignan'S Institute Of Management And Technology For Women; Ms. A. Mounika - Vignan'S Institute Of Management And Technology For Women; and Mr. Amol Chetan Badge - Vignan'S Institute Of on August 23, 2026, for Deception-Based Honey-Token Network For Industrial Control Systems With Plausible Process Dynamics.
Inventors include Mr. K. Bharath Reddy - Vignan'S Institute Of Management; Mrs. A. Srilatha - Vignan'S Institute Of Management And; Mrs. Ch. Navaneetha - Vignan'S Institute Of Management; Ms. A. Mounika - Vignan'S Institute Of Management And Technology For Women; and Mr. Amol Chetan Badge - Vignan'S Institute Of Management And Technology For Women.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: Deception-Based Honey-Token Network for Industrial Control Systems with Plausible Process Dynamics Abstract The increasing adoption of Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) networks in critical infrastructure, including power generation, oil and gas, manufacturing, water treatment, and transportation, has significantly expanded the cyberattack surface of operational technology environments. Conventional intrusion detection and perimeter security mechanisms often struggle to detect sophisticated adversaries employing stealth techniques, lateral movement, credential theft, and zero-day exploits. To address these challenges, this invention proposes a Deception-Based Honey Token Network for Industrial Control Systems with Plausible Process Dynamics, an intelligent cybersecurity framework that combines deception technology, digital process simulation, artificial intelligence, and adaptive threat intelligence to proactively detect, analyze, and contain cyberattacks targeting industrial environments. The proposed framework deploys distributed honey-tokens, decoy programmable logic controllers (PLCs), human-machine interfaces (HMIs), SCADA servers, engineering workstations, historians, industrial databases, and virtual field devices that accurately emulate realistic industrial processes and communication behavior. Dynamic process simulation models continuously generate plausible sensor values, actuator responses, control logic execution, equipment states, production schedules, and network traffic that closely resemble actual operational conditions, making deception assets indistinguishable from genuine industrial infrastructure. The system supports industrial communication protocols including Modbus TCP, DNP3, OPC UA, IEC 61850, PROFINET, EtherNet/IP, and MQTT to maximize compatibility with heterogeneous operational technology networks. Artificial Intelligence algorithms employing machine learning, graph analytics, behavioral modeling, and anomaly detection continuously monitor interactions with deception assets to identify reconnaissance activities, unauthorized access attempts, privilege escalation, malware propagation, insider threats, ransomware behavior, and advanced persistent threats. Adaptive deception policies automatically modify honey-token placement, process behavior, network topology, and attack surfaces based on evolving threat intelligence to maximize attacker engagement while minimizing operational risk. A Large Language Model (LLM)-based cybersecurity reasoning engine interprets attack sequences, industrial process context, security logs, vulnerability intelligence, and compliance requirements to generate explainable incident reports, attack narratives, mitigation recommendations, forensic summaries, and operational risk assessments in natural language. The framework integrates securely with Security Information and Event Management (SIEM), Security Orchestration, Automation and Response (SOAR), industrial firewalls, endpoint detection platforms, and cloud-edge monitoring infrastructure for centralized security management and automated incident response. Experimental evaluations demonstrate that the proposed deception-based framework significantly improves early attack detection, threat attribution, forensic visibility, and response effectiveness while reducing false positives compared with conventional industrial intrusion detection systems. By integrating realistic process-aware deception, AI-driven behavioral analytics, adaptive honey-token deployment, and explainable LLM-assisted cyber reasoning within a unified architecture, the proposed invention provides a scalable, resilient, and proactive cybersecurity solution for safeguarding next generation Industrial Control Systems and critical infrastructure against sophisticated cyber threats.
Disclaimer: Curated by HT Syndication.
