DDOA: A Dirichlet-Based Detection Scheme for Opportunistic Attacks in Smart Grid Cyber-Physical System

Li, Beibei; Lu, Rongxing; Wang, Wei; Choo, Kim‐Kwang Raymond

doi:10.1109/tifs.2016.2576898

Cited by 79 publications

(39 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a SSDF attack, some malicious users attempt to send false data to the fusion center with the intention of influencing the fusion center to arrive at a wrong decision. Such an attack is similar to the false data injection attacks in smart grid cyber‐physical system . A number of mitigation strategies have been proposed, such as those reported in other works …”

Section: Introductionmentioning

confidence: 85%

“…Such an attack is similar to the false data injection attacks in smart grid cyber-physical system. 22,23 A number of mitigation strategies have been proposed, such as those reported in other works. [24][25][26] In a PUE attack, an attacker transmits a signal with the same characteristic as the primary user signal, attempting to deceive secondary users and preventing them from accessing the available frequency spectrum.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

Emami

Zarrabi

Jabbarpour

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method).

show abstract

Section: Introductionmentioning

confidence: 85%

mentioning

confidence: 99%

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

Emami

Zarrabi

Jabbarpour

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The detection of DoS attacks has been investigated thoroughly during the recent years, and several approaches have been suggested, see e.g. [19,20,21,22,18,23,24,25]. On the other hand, works about detection of deception attacks are more scarce in the literature since, as discussed by [26], they are subtler than DoS attacks, hence harder to detect.…”

Section: Introductionmentioning

confidence: 99%

Detection of replay attacks in cyber-physical systems using a frequency-based signature

Sánchez

Rotondo

Escobet

et al. 2019

Journal of the Franklin Institute

View full text Add to dashboard Cite

This paper proposes a frequency-based approach for the detection of replay attacks affecting cyber-physical systems (CPS). In particular, the method employs a sinusoidal signal with a time-varying frequency (authentication signal) into the closed-loop system and checks whether the time profile of the frequency components in the output signal are compatible with the authentication signal or not. In order to carry out this target, the couplings between inputs and outputs are eliminated using a dynamic decoupling technique based on vector fitting. In this way, a signature introduced on a specific input channel will affect only the output that is selected to be associated with that input, which is a property that can be exploited to determine which channels are being affected. A bank of band-pass filters is used to generate signals whose energies can be compared to reconstruct an estimation of the time-varying frequency profile. By matching the known frequency profile with its estimation, the detector can provide the information about whether a replay attack is being carried out or not. The design of the signal generator and the detector are thoroughly discussed, and an example based on a quadruple-tank process is used to show the application and effectiveness of the proposed method.

show abstract

“…Considering the importance of line state and the possibility of data modification, many mechanisms have been proposed to detect line outages and identify FDI attacks [5][6][7][8][9][10][11][12]. For example, in [10], the method based on social users information was described, but, further development was required.…”

Section: Introductionmentioning

confidence: 99%

Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid

et al. 2018

View full text Add to dashboard Cite

False data injection (FDI) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches.

show abstract

DDOA: A Dirichlet-Based Detection Scheme for Opportunistic Attacks in Smart Grid Cyber-Physical System

Cited by 79 publications

References 30 publications

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

Detection of replay attacks in cyber-physical systems using a frequency-based signature

Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid

Contact Info

Product

Resources

About