Research Article
Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting
@INPROCEEDINGS{10.1007/978-3-030-23943-5_6, author={Junaid Chaudhry and Uvais Qidwai and Mahdi Miraz}, title={Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting}, proceedings={Emerging Technologies in Computing. Second International Conference, iCETiC 2019, London, UK, August 19--20, 2019, Proceedings}, proceedings_a={ICETIC}, year={2019}, month={7}, keywords={Cyber security SCADA/ICS networks Healthcare SCADA systems Health Level Seven Impulsive Statistical Fingerprinting Data obfuscation Encryption Context-aware security IEEE 11073}, doi={10.1007/978-3-030-23943-5_6} }- Junaid Chaudhry
Uvais Qidwai
Mahdi Miraz
Year: 2019
Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting
ICETIC
Springer
DOI: 10.1007/978-3-030-23943-5_6
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 incorporate 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 cryptographic 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.
