Aerospace and Electronic Systems Magazine May 2017 - 39
Do et al.
Figure 5.
Model-based (parametric) approach for attack detection and isolation.
pair (T, ν), where T is the stopping time at which the final decision
ν, i.e., the fault type, is decided.
The quickest change detection and isolation problem is concerned with the online monitoring of technical processes, which
may unexpectedly undergo a change of state from normal operation to abnormal behavior. The detection problem consists of making a binary decision of whether the system is working normally,
i.e., we choose the null hypothesis 0, or it has been shifted into an
abnormal mode, i.e., we choose the alternative hypothesis 1. The
isolation problem is the generalization of the detection problem to
the case of multiple postchange hypotheses 1,...,K (Figure 5).
Faults and Attacks
It has been shown that FDI tools could be used for detecting and
mitigating the negative impact of cyber attacks on NCSs [8]. However, these tools might be exploited more successfully if one could
ascertain the similarities and the differences between faults and attacks. Both faults and attacks occur at unknown times, both cause
unpredictable changes in the behavior of physical systems, and they
can be modeled as additive signals on both equations of the discrete-time state-space model. Faults and attacks, however, possess
inherently distinct features, making it difficult for traditional FDI
techniques to be directly applied to detect cyber-physical attacks.
The most significant difference between a fault and an attack
lies in that the fault is considered a phenomenon occurring randomly in each component of a system while the attack is an intentional action performed by malicious adversaries. In addition, simultaneous faults are generally assumed to be noncolluding, while
cyber-physical attacks could be performed in a coordinated way.
MAY 2017
Therefore, attacks are thought to cause more catastrophic damage
than faults.
The execution of attacks in a coordinated manner for bypassing
traditional anomaly detectors has been discussed in the following
papers: the replay attack [9], [71]; the false data injection attack
[10], [72]; the zero-dynamics attack [11]; and the covert attack
[12]. Hence, it is necessary to implement some a priori methods
for revealing such undetectable or unidentifiable attacks before applying detection and isolation techniques.
Finally, faults often exist for lengthy periods before their detection and repair, while malicious attacks are assumed to be performed within a short period because of the limited resources of
the adversaries [5], [35], [58], [69]. However, for safety-critical
applications, it is necessary to detect the attacks with the detection
delay upper bounded by a certain prescribed value [105]-[111]. It
is consequently proposed that detection and identification of attacks be formulated as sequential detection and isolation of transient changes in signals [105]-[111] and stochastic-dynamical systems [29], [39], [112].
Model-Based Attack Detection and Isolation
The application of traditional FDI techniques to the detection and
isolation of cyber attacks has received considerable amount of research effort. For instance, the authors in [5] have formulated the
problem of detecting cyber attacks on process control systems as
the fault diagnosis problem. In addition, the security problem of
SCADA water irrigation canals against cyber-physical attacks has
been considered in [34]-[36]. A comprehensive framework has
been proposed in [7] and [73] for detecting and identifying attacks
IEEE A&E SYSTEMS MAGAZINE
39
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