Foundations of a CPS Resilience - October 2018

PI: Xenofon Koutsoukos

HARD PROBLEM(S) ADDRESSED

The goals of this project are to develop the principles and methods for designing and analyzing resilient CPS architectures that deliver required service in the face of compromised components. A fundamental challenge is to understand the basic tenets of CPS resilience and how they can be used in developing resilient architectures. The primary hard problem addressed is resilient architectures. In addition, the work addresses scalability and composability as well as metrics and evaluation. Given recent results about vulnerabilities of Machine Learning algorithms, particular emphasis is given on analyzing CPS with learning-enabled components.

PUBLICATIONS

[1]  Amin Ghafouri, Yengeniy Vorobeychik, and Xenofon Koutsoukos. "Adversarial Regression for Detecting Attacks in Cyber-Physical Systems", 27th International Joint Conference on Artificial Intelligence and 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), Stockholm, Sweden, July 13-19, 2018.

[2]  Himanshu Neema, Bradley Potteiger, Xenofon Koutsoukos, CheeYee Tang, and Keith Stouffer. "Metrics-Driven Evaluation of Cybersecurity for Critical Railway Infrastructure", National Symposium on Resilient Critical Infrastructure, Resilience Week 2018, Denver, CO, August 20-23, 2018.

KEY HIGHLIGHTS

This quarterly report presents two key highlights that demonstrate (1) the foundations of CPS resilience by integrating redundancy, diversity, and hardening and (2) adversarial machine learning for learning-enabled components in CPS.

Highlight 1: Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an

issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost [1].

[1]  Aron Laszka, Waseem Abbas, Yevgeniy Vorobeychik, and Xenofon Koutsoukos. Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening. IEEE International Conference on Industrial Internet (ICII 2018). Bellevue, WA, October 21-23, 2018.

Highlight 2: Adversarial Regression for Detecting Attacks in Cyber-Physical Systems

Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected. Detection of attacks on sensors is crucial to mitigate this issue. We study supervised regression as a means to detect anomalous sensor readings, where each sensor’s measurement is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to stealthy attacks, which carefully modify readings of compromised sensors to cause desired damage while remaining undetected. Next, we model the interaction between the CPS defender and attacker as a Stackelberg game in which the defender chooses detection thresholds, while the attacker deploys a stealthy attack in response. We present a heuristic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate [2].

[2]  Amin Ghafouri, Yengeniy Vorobeychik, and Xenofon Koutsoukos. "Adversarial Regression for Detecting Attacks in Cyber-Physical Systems", 27th International Joint Conference on Artificial Intelligence and 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), Stockholm, Sweden, July 13-19, 2018.

COMMUNITY ENGAGEMENTS

Our research was presented in two conferences: IJCAI-ECAI 2018 and National Symposium on Resilient Critical Infrastructure.

Participation in the 9th annual Computational Cybersecurity in Compromised Environments (C3E) workshop including two student research posters focusing on security and resilience problems in CPS with learning-enabled components.

Technical meeting about security and resilience of CPS with Dr. James Peery, Associate Laboratory Director of Global Security and Kendal Card, Division Director, DOE-In Programs, Global Security, Oak Ridge National Laboratory.

EDUCATIONAL ADVANCES

RoboScape

We have developed Roboscape, a collaborative, networked robotics environment that makes key ideas in computer science accessible to groups of learners in informal learning spaces and K12 classrooms. RoboScape is built on top of NetsBlox from Vanderbilt University, an open-source, networked, visual programming environment based on Snap!that is specifically designed to introduce students to distributed computation and computer networking. RoboScape provides a twist on the state of the art of robotics learning platforms. First, a user's program controlling the robot runs in the browser and not on the robot. There is no need to download the program to the robot and hence, development and debugging become much easier. Second, the wireless communication between a student's program and the robot can be overheard by the programs of the other students. This makes cybersecurity an immediate need that students realize and can work to address. We have designed and delivered a cybersecurity summer camp to 24 students in grades between 7 and 12. The technology behind RoboScape, the hands-on curriculum of the camp and the lessons learned are presented in [3].

[3] Akos Ledeczi et al., Teaching Cybersecurity with Networked Robots, SIGCSE 2019. Accepted for publication.