TY - JOUR
T1 - Malicious data deception attacks against power systems
T2 - A new case and its detection method
AU - Du, Dajun
AU - Chen, Rui
AU - Li, Xue
AU - Wu, Lei
AU - Zhou, Peng
AU - Fei, Minrui
N1 - Publisher Copyright:
© The Author(s) 2018.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Power systems usually employ bad data detection (BDD) to avoid faulty measurements caused by their anomalies, and hence can ensure the security of the state estimation of power systems. However, recently BDD has been found vulnerable to malicious data deception attacks submerged in big data. Such attacks can purposely craft sparse measurement values (i.e. attack vectors) to mislead power estimates, while not posing any anomalies to the BDD. Some related work has been proposed to emphasize this attack. In this paper, a new malicious data deception attack by considering a practical attacking situation is investigated, where the attacker has limited resources for corrupting measurements. In this case, attackers generate attack vectors with less sparsity to evade conventional BDD, while using a convex optimization method to balance the sparsity and magnitude of attack vectors. Accordingly, the effects of such an attack on operational costs and the risks of power systems are analysed in detail. Moreover, according to security evaluation for individual measurements, such attacks can be detected with high probability by just securing one critical measurement. Numerical simulations illustrate the effectiveness of the proposed new attack case and its detection method.
AB - Power systems usually employ bad data detection (BDD) to avoid faulty measurements caused by their anomalies, and hence can ensure the security of the state estimation of power systems. However, recently BDD has been found vulnerable to malicious data deception attacks submerged in big data. Such attacks can purposely craft sparse measurement values (i.e. attack vectors) to mislead power estimates, while not posing any anomalies to the BDD. Some related work has been proposed to emphasize this attack. In this paper, a new malicious data deception attack by considering a practical attacking situation is investigated, where the attacker has limited resources for corrupting measurements. In this case, attackers generate attack vectors with less sparsity to evade conventional BDD, while using a convex optimization method to balance the sparsity and magnitude of attack vectors. Accordingly, the effects of such an attack on operational costs and the risks of power systems are analysed in detail. Moreover, according to security evaluation for individual measurements, such attacks can be detected with high probability by just securing one critical measurement. Numerical simulations illustrate the effectiveness of the proposed new attack case and its detection method.
KW - Smart grid
KW - bad data detection (BDD)
KW - detection strategy
KW - line overload risk
KW - malicious data deception attack
KW - optimal power flow
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U2 - 10.1177/0142331217740622
DO - 10.1177/0142331217740622
M3 - Article
AN - SCOPUS:85045250536
SN - 0142-3312
VL - 41
SP - 1590
EP - 1599
JO - Transactions of the Institute of Measurement and Control
JF - Transactions of the Institute of Measurement and Control
IS - 6
ER -