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Optimal Denial-of-Service Attacks Against Partially-Observable Real-Time Monitoring Systems

Saad Kriouile, Mohamad Assaad, Amira Alloum, Touraj Soleymani

TL;DR

This paper investigates the impact of denial-of-service attacks on the status updating of a cyber-physical system with one or more sensors connected to a remote monitor via unreliable channels and derives jamming policies that strike a balance between the degradation of the system's performance and the conservation of the adversary's energy.

Abstract

In this paper, we investigate the impact of denial-of-service attacks on the status updating of a cyber-physical system with one or more sensors connected to a remote monitor via unreliable channels. We approach the problem from the perspective of an adversary that can strategically jam a subset of the channels. The sources are modeled as Markov chains, and the performance of status updating is measured based on the age of incorrect information at the monitor. Our objective is to derive jamming policies that strike a balance between the degradation of the system's performance and the conservation of the adversary's energy. For a single-source scenario, we formulate the problem as a partially-observable Markov decision process, and rigorously prove that the optimal jamming policy is of a threshold form. We then extend the problem to a multi-source scenario. We formulate this problem as a restless multi-armed bandit, and provide a jamming policy based on the Whittle's index. Our numerical results highlight the performance of our policies compared to baseline policies.

Optimal Denial-of-Service Attacks Against Partially-Observable Real-Time Monitoring Systems

TL;DR

This paper investigates the impact of denial-of-service attacks on the status updating of a cyber-physical system with one or more sensors connected to a remote monitor via unreliable channels and derives jamming policies that strike a balance between the degradation of the system's performance and the conservation of the adversary's energy.

Abstract

In this paper, we investigate the impact of denial-of-service attacks on the status updating of a cyber-physical system with one or more sensors connected to a remote monitor via unreliable channels. We approach the problem from the perspective of an adversary that can strategically jam a subset of the channels. The sources are modeled as Markov chains, and the performance of status updating is measured based on the age of incorrect information at the monitor. Our objective is to derive jamming policies that strike a balance between the degradation of the system's performance and the conservation of the adversary's energy. For a single-source scenario, we formulate the problem as a partially-observable Markov decision process, and rigorously prove that the optimal jamming policy is of a threshold form. We then extend the problem to a multi-source scenario. We formulate this problem as a restless multi-armed bandit, and provide a jamming policy based on the Whittle's index. Our numerical results highlight the performance of our policies compared to baseline policies.
Paper Structure (16 sections, 13 theorems, 47 equations, 4 figures, 2 algorithms)

This paper contains 16 sections, 13 theorems, 47 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Overview and Organization
  3. Single-Source Scenario
  4. Networked System Model
  5. Performance Index and It's Dynamics
  6. Problem Statement
  7. Optimal Jamming Policy
  8. Closed-Form Expression of the Optimal Solution
  9. Multi-Source Scenario
  10. Problem Statement
  11. Lagrangian Relaxation
  12. Whittle's Index Jamming Policy
  13. Numerical Results
  14. Single-Source Scenario
  15. Multi-Source Scenario
  16. ...and 1 more sections

Key Result

Lemma 1

$s_i$ is an increasing function with respect to $i$

Figures (4)

  • Figure 1: The state transition under a threshold jamming policy with parameter $s_n$, where the state here is the EAoII.
  • Figure 2: Comparison between the optimal policy and a random policy in terms of the average reward.
  • Figure 3: Threshold value of the optimal jamming policy as a function of $\lambda$
  • Figure 4: Comparison between the Whittle index policy (WIP) and a random policy in terms of the average AoII.

Theorems & Definitions (19)

  • Lemma 1
  • Lemma 2
  • Theorem 1
  • Remark 1
  • Proposition 1
  • Proposition 2
  • Proposition 3
  • Definition 1
  • Lemma 3
  • Lemma 4
  • ...and 9 more