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STARDIS: Strategic Scheduling and Deceptive Signaling for Satellite Intrusion Detection System Deployment

Yuzhou Xiao, Linan Huang, Jiachen Sun, Peilong Liu, Chunxiao Jiang, Linling Kuang

TL;DR

A novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution and introduces a deception mechanism using Bayesian persuasion theory to counter intelligent adversaries interception.

Abstract

Satellite communication networks operate under stringent computational constraints and are susceptible to sophisticated cyberattacks. This paper introduces a novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution. In the long-term loop, the ground segment processes historical data to estimate key statistical parameters of the task environment. Additionally, we incorporate the time-varying characteristics of satellite wireless links to account for the dynamic communication context. In the short-term loop, the satellite employs a receding horizon optimization that models dynamic task arrivals and maximizes a utility function considering detection rates and resource costs. To counter intelligent adversaries interception, we introduce a deception mechanism using Bayesian persuasion theory. By strategically manipulating the short-term action sequences in the telemetry downlink, we mislead an external attacker's beliefs. We mathematically model the attacker's optimal response under channel uncertainty and demonstrate that our framework significantly reduces attacker utility. The approach's effectiveness is formally proven using Lyapunov theory.

STARDIS: Strategic Scheduling and Deceptive Signaling for Satellite Intrusion Detection System Deployment

TL;DR

A novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution and introduces a deception mechanism using Bayesian persuasion theory to counter intelligent adversaries interception.

Abstract

Satellite communication networks operate under stringent computational constraints and are susceptible to sophisticated cyberattacks. This paper introduces a novel defense framework that decouples security optimization into ground-based analysis and onboard real-time execution. In the long-term loop, the ground segment processes historical data to estimate key statistical parameters of the task environment. Additionally, we incorporate the time-varying characteristics of satellite wireless links to account for the dynamic communication context. In the short-term loop, the satellite employs a receding horizon optimization that models dynamic task arrivals and maximizes a utility function considering detection rates and resource costs. To counter intelligent adversaries interception, we introduce a deception mechanism using Bayesian persuasion theory. By strategically manipulating the short-term action sequences in the telemetry downlink, we mislead an external attacker's beliefs. We mathematically model the attacker's optimal response under channel uncertainty and demonstrate that our framework significantly reduces attacker utility. The approach's effectiveness is formally proven using Lyapunov theory.
Paper Structure (40 sections, 3 theorems, 25 equations, 10 figures, 4 tables, 2 algorithms)

This paper contains 40 sections, 3 theorems, 25 equations, 10 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Resource-Aware Intrusion Detection in Satellite Networks
  4. Game-Theoretic Deception for Proactive Defense
  5. System Modeling and Architecture
  6. Two-Timescale Architecture
  7. Unified Task and Queue Model
  8. Resource Demand and System State Definition
  9. Time-Varying Wireless Channel Model
  10. Large-Scale and Small-Scale Fading
  11. Dynamic Link Quality and Erasure
  12. Dynamic Latency and Information Age
  13. Adversarial Attack Model
  14. STAR: Strategic Resource Scheduling Optimization
  15. IDS Performance and Utility Function
  16. ...and 25 more sections

Key Result

Theorem 1

Consider a time-varying channel where the signal erasure probability $P_{out}(\gamma(t))$ varies with the channel state $\gamma(t)$. Let $\Pi_{static}$ be the set of channel-agnostic signaling policies satisfying a fixed credibility constraint $C$, and $\Pi_{adapt}$ be the set of channel-adaptive po provided that the channel variance is non-zero. The optimal policy $\pi^*$ exhibits a "water-fillin

Figures (10)

  • Figure 1: The necessity of deploying IDS in the space segment.
  • Figure 2: Overview of the STARDIS proactive defense framework. The ground-based STAR module optimally co-schedules mission and security tasks to determine a true security policy. This policy informs the space-based DIS module, which uses Bayesian persuasion to generate deceptive public signals. These signals manipulate a rational attacker's beliefs, steering their actions toward outcomes that are advantageous for the defender and feeding back into the defense cycle.
  • Figure 3: The proposed two-timescale architecture. The ground segment performs long-term parameter estimation and uplinks model parameters to the satellite. Onboard, the space segment employs short-term receding horizon optimization to generate real-time action sequences. The framework considers an internal threat accessing the policy and an external attacker intercepting the telemetry.
  • Figure 4: Task classification for 3 dimensions.
  • Figure 5: The proposed unified scheduling model. Periodic and aperiodic tasks arrive in a common queue $Q$. Feasible tasks are admitted to buffers, designated as Ready Workload ($RW$), for heterogeneous resources. High-priority tasks can preempt ongoing low-priority tasks, ensuring immediate response to critical events. The timing parameters for any task $S_j$ are shown below.
  • ...and 5 more figures

Theorems & Definitions (10)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Theorem 1: Optimality of Channel-Adaptive Credibility Allocation
  • Lemma 1: Positive Definiteness
  • proof
  • Theorem 2: Monotonic Decrease and Convergence
  • proof
  • Remark 5