| 1. |
- Faramondi, L., et al.
(author)
-
A Hardware-in-the-Loop Water Distribution Testbed Dataset for Cyber-Physical Security Testing
- 2021
-
In: IEEE Access. - 2169-3536. ; 9, s. 122385-1223896
-
Journal article (peer-reviewed)abstract
- This paper presents a dataset to support researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To this end, data were acquired from a hardware-in-the-loop Water Distribution Testbed (WDT) which emulates water flowing between eight tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed of a real subsystem that is virtually connected to a simulated one. The proposed dataset encompasses both physical and network data in order to highlight the consequences of attacks in the physical process as well as in network traffic behaviour. Simulations data are organized in four different acquisitions for a total duration of 2 hours by considering normal scenario and multiple anomalies due to cyber and physical attacks.
|
|
| 2. |
- Faramondi, L., et al.
(author)
-
Evaluating Machine Learning Approaches for Cyber and Physical Anomalies in SCADA Systems
- 2023
-
In: Proc. IEEE Int. Conf. Cyber Security Resilience, CSR. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350311709 ; , s. 412-417
-
Conference paper (peer-reviewed)abstract
- In recent years, machine learning (ML) techniques have been widely adopted as anomaly-based Intrusion Detection System in order to evaluate cyber and physical attacks against Industrial Control Systems. Nevertheless, a performance comparison of such techniques applied to multiple Cyber-Physical Systems datasets is still missing. In light of this, we propose a comparative study about the performance of four supervised ML-algorithms, Random Forest, k-nearest-Neighbors, Support-Vector-Machine and Naïve-Bayes, applied to three different publicly available datasets from water testbeds. Specifically, we consider three different scenarios where we evaluate: (1) the ability to detect cyber and physical anomalies with respect to the nominal samples, (2) the ability to detect specific types of cyber and physical attacks and (3) the ability to recognize unforeseen attacks without providing any previous knowledge about them. Results show the effectiveness of the ML-techniques in identifying cyber and physical anomalies under some assumptions about their effects on the process dynamics.
|
|
