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Hong Zeng, Yuanhao Zhao, Tianjian Wang and Jundong Zhang
False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the detec...
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Iffat Gheyas, Gregory Epiphaniou, Carsten Maple and Subhash Lakshminarayana
The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vul...
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Moslem Dehghani, Taher Niknam, Mohammad Ghiasi, Pierluigi Siano, Hassan Haes Alhelou and Amer Al-Hinai
Cyber-physical threats as false data injection attacks (FDIAs) in islanded smart microgrids (ISMGs) are typical accretion attacks, which need urgent consideration. In this regard, this paper proposes a novel cyber-attack detection model to detect FDIAs b...
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Dustin M. Mink, Jeffrey McDonald, Sikha Bagui, William B. Glisson, Jordan Shropshire, Ryan Benton and Samuel Russ
Modern-day aircraft are flying computer networks, vulnerable to ground station flooding, ghost aircraft injection or flooding, aircraft disappearance, virtual trajectory modifications or false alarm attacks, and aircraft spoofing. This work lays out a da...
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This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of...
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Daniel S. Berman, Anna L. Buczak, Jeffrey S. Chavis and Cherita L. Corbett
This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural ...
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