| Title | Integrating Design and Data Centric Approaches to Generate Invariants for Distributed Attack Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Umer, Muhammad Azmi, Mathur, Aditya, Junejo, Khurum Nazir, Adepu, Sridhar |
| Conference Name | Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5394-6 |
| Keywords | association rule mining, critical infrastructure, cyber-physical attacks, cybersecurity, distributed attack detection, E-Government, Human Behavior, machine learning, policy-based governance, pubcrawl, resilience, Resiliency, water treatment plant |
| Abstract | Process anomaly is used for detecting cyber-physical attacks on critical infrastructure such as plants for water treatment and electric power generation. Identification of process anomaly is possible using rules that govern the physical and chemical behavior of the process within a plant. These rules, often referred to as invariants, can be derived either directly from plant design or from the data generated in an operational. However, for operational legacy plants, one might consider a data-centric approach for the derivation of invariants. The study reported here is a comparison of design-centric and data-centric approaches to derive process invariants. The study was conducted using the design of, and the data generated from, an operational water treatment plant. The outcome of the study supports the conjecture that neither approach is adequate in itself, and hence, the two ought to be integrated. |
| URL | http://doi.acm.org/10.1145/3140241.3140248 |
| DOI | 10.1145/3140241.3140248 |
| Citation Key | umer_integrating_2017 |
Integrating Design and Data Centric Approaches to Generate Invariants for Distributed Attack Detection