Welcome to the homepage of the group for Security and Performance of Networked Systems at UCLouvain.
The group is part of the department of computer science INGI
at the ICTEAM research institute.
Our teaching activities are embedded in the Louvain School of Engineering (EPL).
The group's mission is to improve the security of modern networked systems and to contribute to the understanding of their performance and behavior.
To this end, we develop innovative methods and techniques to monitor such systems and to detect and prevent attacks against them.
We also perform measurements that help to understand the impact of new protocols and services.
Our research activities target, among others, the following fields:
- Intrusion detection and prevention for the Internet of Things (IoT)
- Intrusion detection and prevention for Industrial Control Systems/SCADA
- Software Defined Networking (SDN) for security and network monitoring
- Flow monitoring
- Performance of Blockchain infrastructures
The following courses are taught by the group at UCLouvain:
- Computer System Security (LINGI2347, 5 ECTS): Overview
- Mobile and Embedded Computing (LINGI2146, 5 ECTS): Overview
- Architecture and Performance of Computer Systems (LINGI 2241, 6 ECTS): Overview
- Informatique 2 (LEPL1402, 5 ECTS): Overview (in French)
We have also given training courses on cyber-security, for example at the eurometropolitan e-Campus.
A Public Network Trace of a Control and Automation System
The increasing number of attacks against industrial automation systems and their monitoring and control infrastructures (SCADA, DCS etc.)
have demonstrated that there is a need to secure those systems. The solution privileged by many researchers is the use of
network-based intrusion detection systems (N-IDS). Unfortunately, validating an N-IDS for automation
systems is difficult since getting access to a real and large system for experimentation
is almost impossible.
In this report, we describe and characterize a public traffic dataset collected at
the HVAC management system of a university campus. Although the dataset contains only packet headers, we
believe that it can help researchers, in particular designers of flow-based IDS, to validate their solutions.