FIRED: Frequent Inertial Resets with Diversification for Emerging Commodity Cyber-Physical Systems
February 21, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Miguel Arroyo, Hidenori Kobayashi, Simha Sethumadhavan, Junfeng Yang
arXiv ID
1702.06595
Category
eess.SY: Systems & Control (EE)
Cross-listed
cs.CR
Citations
29
Venue
arXiv.org
Last Checked
1 month ago
Abstract
A Cyber-Physical System (CPS) is defined by its unique characteristics involving both the cyber and physical domains. Their hybrid nature introduces new attack vectors, but also provides an opportunity to design new security defenses. In this paper, we present a new domain-specific security mechanism, FIRED, that leverages physical properties such as inertia of the CPS to improve security. FIRED is simple to describe and implement. It goes through two operations: Reset and Diversify, as frequently as possible -- typically in the order of seconds or milliseconds. The combined effect of these operations is that attackers are unable to gain persistent control of the system. The CPS stays safe and stable even under frequent resets because of the inertia present. Further, resets simplify certain diversification mechanisms and makes them feasible to implement in CPSs with limited computing resources. We evaluate our idea on two real-world systems: an engine management unit of a car and a flight controller of a quadcopter. Roughly speaking, these two systems provide typical and extreme operational requirements for evaluating FIRED in terms of stability, algorithmic complexity, and safety requirements. We show that FIRED provides robust security guarantees against hijacking attacks and persistent CPS threats. We find that our defense is suitable for emerging CPS such as commodity unmanned vehicles that are currently unregulated and cost sensitive.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Systems & Control (EE)
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey
R.I.P.
๐ป
Ghosted
Wireless Network Design for Control Systems: A Survey
R.I.P.
๐ป
Ghosted
Learning-based Model Predictive Control for Safe Exploration
R.I.P.
๐ป
Ghosted
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
R.I.P.
๐ป
Ghosted
Novel Multidimensional Models of Opinion Dynamics in Social Networks
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted