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arxiv: 2606.25744 · v1 · pith:AK2LRXO3new · submitted 2026-06-24 · 📡 eess.SP

Networked Control System Under Controller-Actuator Channel Jamming

Chen Quan , Geethu Joseph , Gourab Ghatak This is my paper

Pith reviewed 2026-06-25 19:49 UTC · model grok-4.3

classification 📡 eess.SP
keywords networked control systemsjamming attacksadaptive jammingevent-triggered controlwireless channelscontroller-actuator channeldefense schemes
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The pith

An adaptive jammer observing transmission successes can degrade control performance with limited budget, but an event-triggered defense reduces the impact even without channel knowledge.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates jamming attacks on the controller-actuator wireless channel in a networked control system that tolerates occasional missed inputs. It proposes an adaptive jamming strategy where the jammer updates beliefs about channel states based on observed transmission successes or failures and optimizes actions within a budget. To counter this, an event-triggered defense is developed for the controller in scenarios with and without channel state knowledge. Simulations indicate that the jamming significantly affects performance while the defense mitigates it effectively.

Core claim

Optimal adaptive jamming attacks, formed by updating beliefs from transmission outcomes, can significantly degrade control performance even with a limited budget, while an event-triggered defense scheme reduces this impact even when the controller lacks channel state knowledge.

What carries the argument

The belief-forming adaptive jamming strategy that optimizes under limited budget based on transmission success observations, countered by the event-triggered defense scheme.

Load-bearing premise

The jammer can observe the success or failure of each controller transmission to form beliefs about channel states.

What would settle it

An experiment or simulation where the adaptive jamming fails to degrade performance more than a random jamming strategy with the same budget, or where the defense provides no improvement.

Figures

Figures reproduced from arXiv: 2606.25744 by Chen Quan, Geethu Joseph, Gourab Ghatak.

Figure 1
Figure 1. Figure 1: Control system under jamming: At the start of block k, the sensor sends state x(kT ) to the controller via a reliable link. The controller sends aggregated control inputs us ag(t) in batches over time slots within block k to the actuator, disrupted by a jammer, resulting in received inputs ur ag(t). In this paper, we consider the presence of an adaptive jammer in the network, as shown in [PITH_FULL_IMAGE:… view at source ↗
Figure 2
Figure 2. Figure 2: Probability of block controllability as a function of the penalty coefficient λ for different channel states. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 5 10 15 Proposed (G,G) Proposed (G,B) Proposed (B,G) Proposed (B,B) Random jamming w/o feedback Constant jamming [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average number of jamming actions as a function of the penalty coefficient λ for different channel states. single-block optimization, assuming that the controller knows its channel. Here, initial channel state distributions are uniform: π Tx q = π Jm d = 0.5 for q, d ∈ {G, B}. For lower values of λ, our jamming strategy can effectively degrade the block controllability. As λ increases, its impact decreases… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of our jamming strategy, with and without defense, and other strategies: random jamming, constant jamming, and limited-budget constant jamming, across varying block numbers and averaged across different channel states. under different jamming strategies, as well as a comparison between systems with and without the defense scheme. Here, we set π Tx G = π Jm B = 0.3 and π Tx B = π Jm G = 0.7, with… view at source ↗
read the original abstract

Wireless channels in the networked control systems are vulnerable to intentional interference, such as jamming attacks. This paper investigates jamming attacks on the wireless controller actuator channel of a control system that can tolerate occasional control inputs from the controller. We start with a worst case scenario for the jammer where the controller knows its channel state. We develop an adaptive jamming strategy in which the jammer, observing the success or failure of each controller transmission, forms beliefs about its own and the controller actuator channel states. Using this belief, it optimizes its actions under a limited jamming budget. To counter this, we develop an event-triggered defense scheme for the controller in two settings: with and without the knowledge of its channel state. Simulation results show that optimal adaptive jamming attacks can significantly degrade control performance, even with a limited budget, while the defense scheme, even without channel state knowledge, can effectively reduce this impact.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper studies jamming on the controller-actuator wireless channel in a networked control system that tolerates occasional missed inputs. It develops an adaptive jamming policy in which the jammer observes each transmission outcome to maintain beliefs over its own and the C-A channel states, then solves a per-step optimization under a jamming budget. An event-triggered defense is proposed for the controller both with and without channel-state knowledge. Simulations are used to show that the adaptive jamming degrades performance even with limited budget while the defense reduces the impact.

Significance. If the results hold, the work adds to the literature on secure networked control by combining belief-based adaptive attack design with event-triggered defense. The simulation evidence for degradation and mitigation is a concrete strength that supports the practical relevance of the claims.

major comments (2)
  1. [Adaptive jamming strategy section] Adaptive jamming strategy section: The belief recursion and per-step optimization are constructed under the assumption that the jammer perfectly observes the success or failure of every controller transmission. All reported simulation results on performance degradation are generated in this perfect-observation regime; the manuscript provides no analysis or additional simulations under noisy or partial observations, so the load-bearing claim that 'optimal adaptive jamming attacks can significantly degrade control performance' remains conditional on this strong assumption.
  2. [Simulation results section] Simulation results section: The quantitative degradation and mitigation claims rest on the specific belief-update and optimization procedure; without an explicit statement of the channel model parameters, belief initialization, and budget values used in the figures, it is not possible to assess whether the reported effect sizes are robust or sensitive to those choices.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the scope and presentation of our results. We address each major comment below.

read point-by-point responses

  1. Referee: [Adaptive jamming strategy section] Adaptive jamming strategy section: The belief recursion and per-step optimization are constructed under the assumption that the jammer perfectly observes the success or failure of every controller transmission. All reported simulation results on performance degradation are generated in this perfect-observation regime; the manuscript provides no analysis or additional simulations under noisy or partial observations, so the load-bearing claim that 'optimal adaptive jamming attacks can significantly degrade control performance' remains conditional on this strong assumption.

    Authors: The adaptive jamming strategy is explicitly developed under the perfect-observation assumption, which we present as the worst-case scenario for the jammer (see Section III). This modeling choice is stated upfront and the performance-degradation claims are made under that regime. We agree that the results are conditional on perfect observation and will revise the manuscript to emphasize this scope more clearly in the abstract, introduction, and conclusion. Extending the belief recursion and optimization to noisy or partial observations is a natural next step but lies outside the current contribution; we will note it as future work rather than add new simulations at this stage. revision: partial

  2. Referee: [Simulation results section] Simulation results section: The quantitative degradation and mitigation claims rest on the specific belief-update and optimization procedure; without an explicit statement of the channel model parameters, belief initialization, and budget values used in the figures, it is not possible to assess whether the reported effect sizes are robust or sensitive to those choices.

    Authors: We acknowledge that the simulation section does not list all numerical parameters (channel transition probabilities, initial belief vectors, and exact budget values) in the text or captions. In the revised manuscript we will add a dedicated simulation-parameters subsection (or table) that reports these values for every figure, enabling readers to reproduce the experiments and evaluate sensitivity. revision: yes

Circularity Check

0 steps flagged

No significant circularity; models and simulations are self-contained

full rationale

The paper constructs explicit adaptive jamming policies from the jammer's observation model and budget constraint, then evaluates them via simulation against an event-triggered defense. No equations reduce a claimed prediction to a fitted input by construction, no self-citation chain bears the central result, and no ansatz or uniqueness claim is smuggled in. The derivation chain (belief update → per-step optimization → performance metric) remains independent of the reported outcomes.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; all technical details are absent.

pith-pipeline@v0.9.1-grok · 5678 in / 964 out tokens · 22294 ms · 2026-06-25T19:49:08.655047+00:00 · methodology

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Reference graph

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