Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids

@article{Lakshminarayana2022DataDrivenDA,
  title={Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids},
  author={Subhash Lakshminarayana and Saurav Sthapit and Carsten Maple},
  journal={ArXiv},
  year={2022},
  volume={abs/2110.00667}
}
Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems in an effort to provide advanced services and resource efficiency. However, large-scale IoT-based load-altering attacks (LAAs) can have a serious impact on power grid operations such as destabilizing the grid’s control loops. Timely detection and identification of any compromised nodes is important to minimize the adverse effects of these attacks on power grid operations. In this work, we… 
Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach
By manipulating tens of thousands of internet-of-things (IoT) enabled high-wattage electrical appliances (e.g., WiFi-controlled air-conditioners), large-scale load-altering attacks (LAAs) can cause
Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch
—Large-scale Load-Altering Attacks (LAAs) against Internet-of-Things (IoT) enabled high-wattage electrical appliances (e.g., wifi-enabled air-conditioners, electric vehicles, etc.) pose a serious
Load-Altering Attacks Against Power Grids Under COVID-19 Low-Inertia Conditions
The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant
Reduced Order Dynamical Models For Complex Dynamics in Manufacturing and Natural Systems Using Machine Learning
TLDR
This work uses a grey-box ML method with a standard nonlinear optimization approach to identify relevant models of governing dynamics as ODEs using the data simulated from mechanistic models, and modified the ML approach to include the effect of past dynamics, which gives non-linear ODE.

References

SHOWING 1-10 OF 37 REFERENCES
Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism
TLDR
An optimization model is proposed to characterize the behavior of one type of FDI attack that compromises the limited number of state measurements of the power system for electricity theft and achieves high accuracy.
Distributed Internet-Based Load Altering Attacks Against Smart Power Grids
TLDR
This paper identifies a variety of practical loads that can be volnurable to Internet-based load altering attacks and overview a collection of defense mechanisms that can help in blocking these attacks or minimizing the damage caused by them.
On the Feasibility of Load-Changing Attacks in Power Systems During the COVID-19 Pandemic
TLDR
This feasibility study evaluates load-changing attack scenarios using real load consumption data from the New York Independent System Operator (NYISO) and shows that an attacker with sufficient knowledge and resources could be capable of producing frequency stability problems, with frequency excursions going up to 60.5 Hz and 63.4 Hz.
BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid
TLDR
This work reveals a new class of potential attacks on power grids called the Manipulation of demand via IoT (MadIoT) attacks that can leverage such a botnet in order to manipulate the power demand in the grid.
Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
TLDR
A data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems using autoencoders for efficient dimension reduction and feature extraction of measurement datasets and integrates the autoen coders into an advanced generative adversarial network framework.
Detecting dynamic load altering attacks: A data-driven time-frequency analysis
TLDR
Depending on the attack implementation and the type of data available, both time-domain and frequency-domain detection analysis may be needed to ensure accurate attack detection.
False data injection attacks against state estimation in electric power grids
TLDR
A new class of attacks, called false data injection attacks, against state estimation in electric power grids are presented, showing that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection.
Grid Shock: Coordinated Load-Changing Attacks on Power Grids: The Non-Smart Power Grid is Vulnerable to Cyber Attacks as Well
TLDR
It is shown that an adversary does not have to rely on smart grid features to modulate power consumption given that an adequate communication infrastructure for striking the (legacy) power grid is currently nearly omnipresent: the Internet to whom more and more power-consuming devices are connected.
Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning
TLDR
The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively.
Machine Learning Methods for Attack Detection in the Smart Grid
TLDR
Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
...
...