SEBE PhD Research Project Portfolio

Glasgow Caledonian University
SEBE PhD Research Project Portfolio
Project Reference Number
School/Institute/Research Group
School of Engineering and Built Environment / Networks and Communications
Research Discipline
Smart grid, cyber security, critical infrastructure, intrusion detection systems
(IDS), intrusion prevention systems (IPS)
Project Title
Intrusion Detection Systems for Smart Grid
Research Project Summary
Cyber-security for critical infrastructure is a very concerning issue because of
emerging cyber-threats and security incidents targeting critical infrastructures
all over the world. In 2011 McAfee reported over 60% of critical infrastructure
companies regularly found malware designed to attack their systems. Smart
grid is the most fundamental cyber-physical infrastructures of the humankind
and modern society. Cyber-security must address not only deliberate attacks,
for example from disgruntled employees, industrial espionages, and terrorists,
but also accidental compromises of the cyber infrastructure due to user
negligent, user errors, equipment failure, and natural disasters. Vulnerabilities
may allow an attacker to penetrate a system, get access to a control center,
and modify load conditions to destabilize a critical infrastructure in
unpredictable ways leading to serious results or disaster. Furthermore, the
nature of IT systems in Industrial Control Systems (ICS) such as smart grid is
different from conventional IT systems. Therefore, security solutions that are
applicable to IT systems might not fit perfectly into ICS. In ICS industry, cyber
security issues were not considered to a great extent, or not at all during
designing phase of a product.
The aim of this PhD project is to investigate and develop novel intrusion
detection system (IDS) that suitable for smart grid. IDS is essential to protect
any critical infrastructure networks from cyber-attacks. The project will
develop hybrid methods for IDS: signature-based, anomaly-detection and
behavioral-based. In particular, the use of machine learning algorithms, such
as Bayesian classification, shall be investigated for the development of new
generation IDS for smart grid.
A strong and effective IDS with high-accuracy detection and low false positives
rates is invaluable for any critical infrastructure such as smart grid because it
can prevent revenue loss, prevent disaster and more importantly save lives.
Supervisory Team
Staff Contact
Dr. Bernardi Pranggono, CCIS/SEBE
Prof. Huaglory Tianfield, CCIS/SEBE
Dr. Hong Yue, Department of Electronic and Electrical Engineering,
University of Strathclyde
Dr. Bernardi Pranggono (