Foundations of a CPS Resilience - January 2021

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. 

PUBLICATIONS

[1]    Waseem Abbas, Mudassir Shabbir, Jiani Li, and Xenofon Koutsoukos. "Interplay Between Resilience and Accuracy in Resilient Vector Consensus in Multi-Agent Networks", 59th Conference on Decision and Control (CDC 2020), December 14-18, 2020.
[2]    Yasin Yazicioglu, Mudassir Shabbir, Waseem Abbas, and Xenofon Koutsoukos. "Strong Structural Controllability of Diffusively Coupled Networks: Comparison of Bounds Based on Distances and Zero Forcing", 59th Conference on Decision and Control (CDC 2020), December 14-18, 2020.
[3]    Jiani Li, Waseem Abbas, and Xenofon Koutsoukos. "Byzantine Resilient Distributed Multi-Task Learning", Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), December 6-12, 2020.
[4]    Zihao Zhan, Zhenkai Zhang, and Xenofon Koutsoukos. "BitJabber: The World's Fastest Electromagnetic Covert Channel", IEEE International Symposium on Hardware Oriented Security and Trust (HOST 2020), December 2020. Nominated for Best Paper and Best Student Paper Award
[5]    Ali Ozdagli and Xenofon Koutsoukos. "Domain Adaptation for Structural Health Monitoring", Annual Conference of the PHM Society (PHM 2020), 2 (1), p. 9, 2020
[6]    Xingyu Zhou, Robert Canady, Yi Li, Xenofon Koutsoukos, and Aniruddha Gokhale. "Overcoming Stealthy Adversarial Attacks on Power Grid Load Predictions Through Dynamic Data Repair", In: Darema F., Blasch E., Ravela S., Aved A. (eds), Dynamic Data Driven Application Systems. DDDAS 2020., Lecture Notes in Computer Science, vol.12312. Springer, Cham.
[7]    Dimitrios Boursinos and Xenofon Koutsoukos. "Improving Prediction Confidence in Learning-Enabled Autonomous Systems", In: Darema F., Blasch E., Ravela S., Aved A. (eds), Dynamic Data Driven Application Systems. DDDAS 2020., Lecture Notes in Computer Science, vol.12312. Springer, Cham.
[8]    Dimitrios Boursinos and Xenofon Koutsoukos. "Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components", Tools and Methods for Competitive Engineering (TMCE 2020), 2020.

KEY HIGHLIGHTS

This quarterly report presents two key highlights that demonstrate (1) byzantine resilient distributed multi-task learning and (2) BitJabber: The world's fastest electromagnetic covert channel.

Highlight 1: Byzantine Resilient Distributed Multi-Task Learning

Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models simultaneously. However, distributed algorithms for learning relatedness among tasks are not resilient in the presence of Byzantine agents. In this work, we present an approach for Byzantine resilient distributed multi-task learning. We propose an efficient online weight assignment rule by measuring the accumulated loss using an agent’s data and its neighbors’ models. A small accumulated loss indicates a large similarity between the two tasks. In order to ensure the Byzantine resilience of the aggregation at a normal agent, we introduce a step for filtering out larger losses. We analyze the approach for convex models and show that normal agents converge resiliently towards the global minimum. Further, aggregation with the proposed weight assignment rule always results in an improved expected regret than the non-cooperative case. Finally, we demonstrate the approach using three case studies, including regression and classification problems, and show that our method exhibits good empirical performance for non-convex models, such as convolutional neural networks. Our results are reported in [1].

[1]    Jiani Li, Waseem Abbas, and Xenofon Koutsoukos. "Byzantine Resilient Distributed Multi-Task Learning", Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), December 6-12, 2020.

Highlight 2: BitJabber: The World's Fastest Electromagnetic Covert Channel

An air-gapped computer is physically isolated from unsecured networks to guarantee effective protection against data exfiltration. Due to air gaps, unauthorized data transfer seems impossible over legitimate communication channels, but in reality many so-called physical covert channels can be constructed to allow data exfiltration across the air gaps. Most of such covert channels are very slow and often require certain strict conditions to work (e.g., no physical obstacles between the sender and the receiver). In this work, we introduce a new physical covert channel named BitJabber that is extremely fast and strong enough to even penetrate concrete walls. We show that this covert channel can be easily created by an unprivileged sender running on a victim’s computer. Specifically, the sender constructs the channel by using only memory accesses to modulate the electromagnetic (EM) signals generated by the DRAM clock. While possessing a very high bandwidth (up to 300,000 bps), this new covert channel is also very reliable (less than 1% error rate). More importantly, this covert channel can enable data exfiltration from an air-gapped computer enclosed in a room with thick concrete walls up to 15 cm. Our results are reported in [2].

[2]    Zihao Zhan, Zhenkai Zhang, and Xenofon Koutsoukos. "BitJabber: The World's Fastest Electromagnetic Covert Channel", IEEE International Symposium on Hardware Oriented Security and Trust (HOST 2020), December 2020.

COMMUNITY ENGAGEMENTS

• Our research was presented in the following conferences: 59th IEEE Conference on Decision and Control (CDC 2020), Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), IEEE International Symposium on Hardware Oriented Security and Trust (HOST 2020), Annual Conference of the PHM Society (PHM 2020), Dynamic Data Driven Application Systems. DDDAS (2020), and Tools and Methods for Competitive Engineering (TMCE 2020).