Glasgow Caledonian University SEBE PhD Research Project Portfolio Project Reference Number SEBE_NCS_BP_1 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 http://www.gcu.ac.uk/ebe/staff/drbernardipranggono/ Prof. Huaglory Tianfield, CCIS/SEBE http://www.gcu.ac.uk/ebe/staff/htianfield/ Dr. Hong Yue, Department of Electronic and Electrical Engineering, University of Strathclyde http://homepages.eee.strath.ac.uk/~hongyue/ Dr. Bernardi Pranggono (b.pranggono@gcu.ac.uk)