Work

A Federated Multi-modal System for Insider Threat Detection in Energy Infrastructure Using Biometric and Cyber Data

Year: 2025

Type: article

Abstract: Insider threats pose significant risks to the operational continuity and security of critical energy infrastructure. This paper presents a federated multi-modal system that integrates biometric and cyber data to detect insider threats with high accuracy while preserving data privacy. The proposed architecture combines facial recognition, keystroke dynamics, and network activity logs using a federated learning framework, enabling decentralized model training across multiple nodes. This approach reduces data exposure risks and supports compliance with privacy regulations. Experimental evaluations on synthetic and real-world datasets demonstrate the system’s effectiveness in identifying anomalous user behavior patterns, outperforming centralized baselines in both detection rate and resilience. The study offers a scalable, privacy-aware solution for securing energy systems against internal cyber-physical threats.

Source: International Journal of Cyber Threat Intelligence and Secure Networking

DOI: 10.55640/ijctisn-v02i01-01

Authors: Dr. Tanvi Das, James D. Walker

Institutions: National Institute of Technology Tiruchirappalli, The University of Texas at Austin

Language: English

Topic: User Authentication and Security Systems

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4411892172

FWCI:

Cites: 5

Cited by:

Related to: 10