DeepAI AI Chat
Log In Sign Up

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

by   Subhash Lakshminarayana, et al.

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 present two data-driven algorithms to detect and identify compromised nodes and the attack parameters of the LAAs. The first, based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, adopts a sparse regression framework to identify attack parameters that best describes the observed dynamics. The second method, based on physics-informed neural networks (PINN), adopts deep neural networks to infer the attack parameters from the measurements. Both methods are presented utilizing edge computing for deployment over decentralized architectures. Extensive simulations performed on IEEE bus systems show that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, especially in systems that exhibit fast dynamics and are effective in detecting and identifying locations of attack in a timely manner.


A Novel Sybil Attack Detection Scheme Based on Edge Computing for Mobile IoT Environment

Internet of things (IoT) connects all items to the Internet through info...

Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks

Large-scale load-altering attacks (LAAs) are known to severely disrupt p...

Load-balanced Service Function Chaining in Edge Computing over FiWi Access Networks for Internet of Things

Service function chaining (SFC) is promising to implement flexible and s...

Battery draining attacks against edge computing nodes in IoT networks

Many IoT devices, especially those deployed at the network edge have lim...

Learning to Attack Powergrids with DERs

In the past years, power grids have become a valuable target for cyber-a...

Detecting Zero-day Controller Hijacking Attacks on the Power-Grid with Enhanced Deep Learning

Attacks against the control processor of a power-grid system, especially...