5 research outputs found
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Outsourcing data storage to the remote cloud can be an economical solution to
enhance data management in the smart grid ecosystem. To protect the privacy of
data, the utility company may choose to encrypt the data before uploading them
to the cloud. However, while encryption provides confidentiality to data, it
also sacrifices the data owners' ability to query a special segment in their
data. Searchable symmetric encryption is a technology that enables users to
store documents in ciphertext form while keeping the functionality to search
keywords in the documents. However, most state-of-the-art SSE algorithms are
only focusing on general document storage, which may become unsuitable for
smart grid applications. In this paper, we propose a simple, practical SSE
scheme that aims to protect the privacy of data generated in the smart grid.
Our scheme achieves high space complexity with small information disclosure
that was acceptable for practical smart grid application. We also implement a
prototype over the statistical data of advanced meter infrastructure to show
the effectiveness of our approach
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
False data injection attack (FDIA) is a critical security issue in power
system state estimation. In recent years, machine learning (ML) techniques,
especially deep neural networks (DNNs), have been proposed in the literature
for FDIA detection. However, they have not considered the risk of adversarial
attacks, which were shown to be threatening to DNN's reliability in different
ML applications. In this paper, we evaluate the vulnerability of DNNs used for
FDIA detection through adversarial attacks and study the defensive approaches.
We analyze several representative adversarial defense mechanisms and
demonstrate that they have intrinsic limitations in FDIA detection. We then
design an adversarial-resilient DNN detection framework for FDIA by introducing
random input padding in both the training and inference phases. Extensive
simulations based on an IEEE standard power system show that our framework
greatly reduces the effectiveness of adversarial attacks while having little
impact on the detection performance of the DNNs