Related papers: Information-Theoretic Attacks in the Smart Grid
Small-Signal Stability (SSS) is crucial for the control of power grids. However, False Data Injection (FDI) attacks against SSS can impact the grid stability, hence, the security of SSS needs to be studied. This paper proposes a formal…
Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large-scale IoT-based load-altering attacks (LAAs) can seriously…
In wireless security, cognitive adversaries are known to inject jamming energy on the victim's frequency band and monitor the same band for countermeasures thereby trapping the victim. Under the class of cognitive adversaries, we propose a…
The learning data requirements are analyzed for the construction of stealth attacks in state estimation. In particular, the training data set is used to compute a sample covariance matrix that results in a random matrix with a Wishart…
Cyber-physical attacks impose a significant threat to the smart grid, as the cyber attack makes it difficult to identify the actual damage caused by the physical attack. To defend against such attacks, various inference-based solutions have…
In this paper, we consider the problem of attack-resilient state estimation, that is to reliably estimate the true system states despite two classes of attacks: (i) attacks on the switching mechanisms and (ii) false data injection attacks…
A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack…
In modern power grids, a local failure or attack can trigger catastrophic cascading failures, which make it challenging to assess the attack vulnerability of power grids. In this Brief, we define the $K$-link attack problem and study the…
Recent research has shown that large-scale Internet of Things (IoT)-based load altering attacks can have a serious impact on power grid operations such as causing unsafe frequency excursions and destabilizing the grid's control loops. In…
The Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary…
Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial losses, maintenance damage, and disruptions in electricity…
Efficient and accurate state estimation is essential for the optimal management of the future smart grid. However, to meet the requirements of deploying the future grid at a large scale, the state estimation algorithm must be able to…
Cyber data attacks are the worst-case interacting bad data to power system state estimation and cannot be detected by existing bad data detectors. In this paper, we for the first time analyze the likelihood of cyber data attacks by…
Cyber-attacks can have severe impacts on critical infrastructures, from outages to economical loss and physical damage to people and environment. One of the main targets of these attacks is the smart grid. In this paper, we propose a new…
With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly…
The goal of this note is to assess whether simple machine learning algorithms can be used to determine whether and how a given network has been attacked. The procedure is based on the $k$-Nearest Neighbor and the Random Forest…
We consider estimating the predictive density under Kullback-Leibler loss in a high-dimensional Gaussian model. Decision theoretic properties of the within-family prediction error -- the minimal risk among estimates in the class…
For reducing threat propagation within an inter-connected network, it is essential to distribute the defense investment optimally. Most electric power utilities are resource constrained, yet how to account for costs while designing threat…
Inferring and comparing complex, multivariable probability density functions is fundamental to problems in several fields, including probabilistic learning, network theory, and data analysis. Classification and prediction are the two faces…
Machine learning models have been shown to be vulnerable to membership inference attacks, i.e., inferring whether individuals' data have been used for training models. The lack of understanding about factors contributing success of these…