Resilience | Kalyan Perumalla

DarkNet Cyber Resilience

Tue, 22 Mar 2022 00:00:00 +0000

DarkNet Cyber Resilience investigates the impacts of cyber stress and its thresholds of impact on alternative timing signals for the US national energy grid.

Cyber Resilience of Alternative Timing for Energy Grids

Overview

This project is focused on performing a systematic evaluation of the impacts of cyber stress on the system. It is developing tools that enable early detection of performance issues and rapid recovery from cyber-related events. A unique advancement of the project lies in going beyond general cybersecurity vulnerabilities and exposures (CVE). This is achieved by evaluating their implications to the cyber resilience specifically with regard to precise effects on the transmission and receipt of timing signals over the complex networks connecting the timing sources to the geographically-distant subscribers.

Key Links

DarkNet Website

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Organization

Sponsor: US Department of Energy (DOE)
- Office: Office of Electricity (OE)
- Program: DarkNet
Prime: Oak Ridge National Laboratory (ORNL)

Selected Publications

DarkNet Cyber Resilience

Two key cyber elements affecting the grid’s timing capability are Cyber Resilience and Cyber Trust. The timing services of DarkNet are systematically subjected to a range of cyber phenomena that stress four key performance factors, namely: Accuracy, Manageability, Telemetry, and Visibility. This analysis is designed to provide insights into four important categories of undesirable cyber phenomena: Loss of View (LoV), Loss of Control (LoC), Manipulation of View (MoV), and Manipulation of Control (MoC).

Kalyan Perumalla, Juan Lopez

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Digital Twin Framework

Sat, 01 Jan 2022 00:00:00 +0000

Our novel Digital Twin Framework (DTF) is designed to improve resilience of critical infrastructure systems by continuously comparing the infrastructure state with automatically generated, AI/ML-based, real-time digital-twin simulation of the system.

Team

Digital Twin Framework Software Architecture

Overview

As the level of automation in critical infrastructure increases, the ability to detect cyber intrusions becomes more crucial and extremely challenging. Recent cyber attacks demonstrate the devastating and widespread affects they can have on critical infrastructure.

DTF is designed specifically to detect and eventually prevent such attacks, with models validated against experimental data from two critical infrastructure experimental emulators – a canal lock system and an electric distribution system – exhibiting very different dynamics. The canal lock system’s digital twin uses a recurrent neural network trained from the experimental data collected via the DTF. A digital twin of the transmission system is created using a commercial real-time power systems simulator and integrated into our DTF along with the hardware, embedded controllers, and live sensor data using the Open Field Message Bus data model, and publish/subscribe communication protocols. A cyber attack is used on both systems to demonstrate the DTF’s detection capability.

Organization

Sponsor: Lab Directed Research and Development, Oak Ridge National Laboratory
Period: 2018-2020

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On the Effectiveness of Recurrent Neural Networks for Live Modeling of Cyber-Physical Systems

We empirically study the effectiveness of Recurrent Neural Network (RNN)-based models as the basis of DT-based resilience and uncover the important characteristics of an RNN-based solution with experimentation on a lab-scale Canal Lock CPS emulator with live validations and attack scenarios. For the first time, we demonstrate actual, real-time use of a RNN-based model as a DT for performing live analysis on an operational CPS.

Srikanth Yoginath, Varisara Tansakul, Supriya Chinthavali, Curtis Taylor, Joshua Hambrick, Philip Irminger, Kalyan Perumalla

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A Digital Twin Framework for Testing, Evaluation and Deployment of Resilient Cyber-physical Systems

We describe an approach to detecting and preventing cyber attacks by continuously comparing the infrastructure state with a real-time digital-twin simulation of it. Specifically, we describe and demonstrate a Digital Twin Framework (DTF) designed specifically to detect and eventually prevent such attacks. The canal lock system’s digital twin uses a recurrent neural network trained from the experimental data collected via the DTF.

Raymond Hink, Mark Buckner, Supriya Chinthavali, Chris Craig, Timothy Daniel, Joel Dawson, Milton Ericson, Joshua Hambrick, Philip Irminger, Ryan Kerekes, Juan Lopez, Kalyan Perumalla, Nicholas Peters, Stacy Prowell, Varisara Tansakul, Curtis Taylor, Bailu Xiao, Srikanth Yoginath

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NetWarp

Sat, 01 Jan 2022 00:00:00 +0000

NetWarp is a novel time-synchronized virtual machine(VM)-based parallel simulation framework that accurately lifts the devices and the network communications to a virtual time plane while retaining full fidelity.

NetWarp

Organization

Sponsors: US Department of Defense
- Office: Army Research Laboratory (ARL)
- Office: Missile Defense Agency (MDA)
- Programs: Computational Sciences, STTR
Prime: Oak Ridge National Laboratory (ORNL)

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summary: Secure Virtual Environment for Cyber Resiliency Validation building on NetWarp technology for cybersecurity in hardware and software of missile defense systems

Virtual Machine-Based Simulation Platform For MANET-Based Cyber Infrastructure

In modeling and simulating complex systems such as mobile ad-hoc networks (MANETs) in defense communications, …

Srikanth Yoginath, Kalyan Perumalla, Brian Henz

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Taming Wild Horses: The Need for Virtual Time-based Scheduling of VMs in Network Simulations

The next generation of scalable network simulators employ virtual machines (VMs) to act as high-fidelity models of traffic producer/consumer nodes…

Srikanth Yoginath, Kalyan Perumalla, Brian Henz

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Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES)…

Srikanth Yoginath, Kalyan Perumalla

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