Federated Deception Intelligence and the ASKOS Solution for Cyber-Resilient Healthcare Infrastructures.

Area of Science:

• Cybersecurity
• Healthcare Technology
• Network Security

Background:

• Healthcare infrastructures face increasing cyber risks from legacy systems, Internet of Medical Things (IoMT) devices, and data sharing complexities.
• Traditional security systems like intrusion detection and Security Information and Event Management (SIEM) are insufficient against sophisticated attacks such as zero-day exploits and lateral movement.

Purpose of the Study:

• To introduce the ASKOS architecture, a novel security framework designed for adaptive and privacy-preserving cyber defense in healthcare.
• To enhance the protection of healthcare infrastructures against advanced and emerging cyber threats.

Main Methods:

• The ASKOS architecture integrates Conpot honeypots, federated machine learning, and software-defined networking (SDN).
• It employs deception telemetry, distributed anomaly detection, and risk-aware orchestration for intelligent security.
• This approach enables privacy-preserving, adaptive cyber defense strategies.

Main Results:

• ASKOS provides a robust defense mechanism against complex cyber threats targeting healthcare systems.
• The framework demonstrates effective integration of deception, machine learning, and network control.
• It facilitates intelligent and compliant security operations within healthcare environments.

Conclusions:

• The ASKOS architecture offers a promising solution for bolstering cybersecurity in healthcare.
• Its federated and deception-enhanced approach addresses limitations of conventional security systems.
• ASKOS enables a more resilient and secure digital healthcare ecosystem.