Detecting intrusions in control systems : A rule of thumb, its justification and illustrations

Abstract

Control systems are exposed to unintentional errors, deliberate intrusions, false data injection attacks, and various other disruptions. In this paper we propose, justify, and illustrate a rule of thumb for detecting, or confirming the absence of, such disruptions. To facilitate the use of the rule, we rigorously discuss background results that delineate the boundaries of the rule's applicability. We also discuss ways to further widen the applicability of the proposed intrusion-detection methodology.

Detecting systematic anomalies affecting systems when inputs are stationary time series

“…We conclude the description of the experimental design with the note that when the inputs (X t ) t∈Z are iid random variables, which is a very special case of the present paper, anomaly detection in systems with δ t = 0 has been studied by Gribkova and Zitikis (2020), with t = 0 by Gribkova and Zitikis (2018), and with arbitrary (δ t , t ) by Gribkova and Zitikis (2019b). In the present paper we extend those iid-based results to scenarios when inputs are governed by stationary time-series models, which is a very important feature from the practical point of view.…”

“…The departure from the earlier explored case of independent and identically distributed (iid) inputs (e.g., Gribkova and Zitikis, 2020) to the herein tackled dependent random inputs, and thus outputs, requires considerable technical innovation and have given rise to notions such as preasonable order and temperate dependence, whose connections to classical notions such as phantom distributions have been illuminated. We note at the outset that the just mentioned parameter p is related the p-th finite moment of inputs, and thus to the tail heaviness of input distributions.…”

Detecting systematic anomalies affecting systems when inputs are stationary time series

“…The departure from the earlier explored by Gribkova and Zitikis 30,31 case of independent and identically distributed (iid) inputs to the herein tackled dependent random inputs and thus outputs requires considerable technical innovation and have given rise to notions such as p-reasonable order and temperate dependence, whose connections to classical notions such as phantom distributions have been illuminated. We note that the just mentioned parameter p is related the pth finite moment of inputs, and thus to the tail heaviness of the input distribution.…”

Special Issue “Risk, Ruin and Survival: Decision Making in Insurance and Finance”

“…Many engineering-related studies employ techniques in the frequency domain, while Gribkova and Zitikis (2018) pursue the task in the time domain. The latter paper is a part of the tetralogy by Gribkova and Zitikis (2018, 2019a, 2019b, 2019c who develop a comprehensive classification and testing methodology for dealing with potential effects of systemic risk on systems at their input and/or output stages. The importance of such research is due to the fact, among other reasons, that even though the decision maker may be aware of the existence of systemic risk and would thus incorporate it into the statistical model, the decision maker cannot be sure that the resulting model complexity is really justified.…”