## 5 Discussion
### Suitability Assessment for Detectors to Large-Scale Plants  
Theoretically, two major factors that impact the performance of an anomaly detector are its rates of detection and false alarms. Several existing methodologies claim that their proposed approaches outperform others in these metrics based on experiments conducted on benchmark datasets or in a simulated environment. However, there is no guarantee that similar performance will be experienced when deployed in large-scale operational systems. For example, consider the work on probabilistic neural networks (PNN) [15]. From the experimental results using the benchmark data, it was found that the average attack detection and false alarm rates were, respectively, 99.29% and 0.002%. However, when the same PNN model is deployed in SWaT, the detection rate is reduced to 1.3%, and the false alarm rate increases to 87.14%. A similar problem was encountered while altering the water level markers of tank T101, which is reported in [2]. Reasons underlying the drastic change in the performance are (i) the dynamic nature of ICS, (ii) the aging factor of components, and (iii) temporal glitches. Thus, one should consider these practical issues while designing and deploying an anomaly detector on large-scale plants. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Differentiators \({}^{\star}\)** & **AICrit in [2]** & **AICrit in CISS2021-OL** \\ \hline Type & Insider & Insider and external \\ \hline Launchers & Authors & Hackers from outside \\ \hline Entry & Network & Network and physical \\ \hline Launch point & SCADA & Network and inside SWaT \\ \hline Targets \({}^{\ddagger}\) & SSSP & SSSP, SSMP, MSSP, and MSMP \\ \hline Launched & 13 & 27 \\ \hline Detected & 13 & 23 \\ \hline False positives & 0 & 0 \\ \hline Process impact & Minor & Major \({}^{\star}\) \\ \hline \end{tabular} \({}^{\star}\) Differentiators are based on cyber and physical attacks. \({}^{\star}\) Some attacks led to catastrophic failures, such as tank overflow and abnormality in the chemical properties of water, resulting in service disruption. \({}^{\ddagger}\) See Section 3.  
\end{table} 
Table 10: Evaluation of AICrit in [2] and this work. Unlike traditional methodologies, AICrit operates over the spatio-temporal dependencies among the components. The interactions across the highly correlated components are accurately learned through the application of machine learning algorithms coupled with design knowledge. Thus, the dynamic nature of ICS has minimal impact on the performance of AICrit. Further, intrinsic parameters, namely UCL, LCL, window size, and count size (refer to Equations (6) and (8)-(10) in [2]) of AICrit prevents the generation of false positives for short-term deviations experienced due to noise and temporal glitches. Thus, the adaptability of AICrit to the above-mentioned factors makes it suitable for large plants. The research questions formulated in Section 1 are re-visited below in the context of the experimental results presented above. **RQ 1:**_How effective is AICrit in detecting the cyber-physical attacks that have an adverse impact on the physical processes of underlying SWaT?_  
As reported in Table 9, AICrit detected all but one attack (A18) that led to an abnormal change in the physical processes of SWaT. Further, the false-positive rate of AICrit during CISS2021-OL was zero. The experimental results discussed in Section 4 indicate that AICrit accurately learned the interactions across SWaT components for the effective detection of process anomalies. **RQ 2:**_How efficient is AICrit in supporting the plant operators toward active incident response and recovery?_  
AICrit can accurately model the normal behavior of each component based on the given state and compare it with the measurements retrieved from the operational SWaT. By doing so, AICrit not only detects the process anomalies accurately but also possesses the ability to report the semantics of the detected anomalies.