Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting

TL;DR

This paper introduces Impulsive Statistical Fingerprinting (ISF), a novel data obfuscation method for SCADA/ICS networks that enhances security against eavesdropping without relying on cryptography, especially suited for unreliable channels.

Contribution

The paper proposes ISF as an alternative to cryptographic security in SCADA systems, leveraging statistical fingerprinting based on pulse length and frequency, with applications in healthcare data security.

Findings

ISF effectively obfuscates SCADA data streams.

ISF can be integrated with HL7 format for healthcare data.

The approach offers a non-cryptographic security solution.

Abstract

While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incor-porate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use crypto-graphic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS.