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arxiv: 2606.03530 · v1 · pith:F7Z4FGMUnew · submitted 2026-06-02 · 💻 cs.CR · cs.NI

Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models

Elias Lunderbye , Sourasekhar Banerjee , Christian Rohner , Andreas Johnsson This is my paper

Pith reviewed 2026-06-28 09:30 UTC · model grok-4.3

classification 💻 cs.CR cs.NI
keywords intrusion detection systemsRPLIoTfoundation modelsMOMENTattack detectionmulti-class classification
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The pith

A fine-tuned foundation model achieves state-of-the-art performance in detecting and classifying attacks on RPL-based IoT networks.

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

The paper tests whether a pre-trained foundation model can be fine-tuned to serve as an intrusion detection system for RPL-based IoT networks. Using data from Cooja simulations that includes normal traffic and four types of attacks, the authors adapt the MOMENT model for classifying both the presence and the specific type of attack. If successful, this would mean that general-purpose time series models can handle specialized security tasks in constrained networks without building everything from scratch. The reported results show detection performance on par with prior methods and good separation between attack classes.

Core claim

By fine-tuning the MOMENT foundation model on RPL statistics collected in simulation, the approach reaches attack detection accuracy comparable to state-of-the-art specialized systems and additionally performs strongly at identifying which specific attack is occurring among Blackhole, DIS flooding, Worst Parent, and Local Repair.

What carries the argument

The MOMENT foundation model fine-tuned for multi-class classification of RPL network statistics.

If this is right

  • The method provides a way to detect attacks without designing attack-specific features.
  • It can distinguish between multiple attack types in one model.
  • Performance remains stable across variations in network configurations.
  • Foundation models offer a reusable base for IDS in IoT settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the simulation data proves representative, the same fine-tuning process could be applied to real deployments with limited additional data.
  • Similar foundation-model approaches may extend to other IoT protocols beyond RPL.
  • Reducing reliance on hand-crafted features could speed up response to new attack variants.

Load-bearing premise

RPL statistics gathered in the Cooja simulator under normal and attack conditions are representative enough of real-world networks for the model to work outside the simulation.

What would settle it

A drop in detection or classification accuracy when the model is evaluated on traces from actual hardware RPL networks instead of Cooja simulations.

Figures

Figures reproduced from arXiv: 2606.03530 by Andreas Johnsson, Christian Rohner, Elias Lunderbye, Sourasekhar Banerjee.

Figure 1
Figure 1. Figure 1: A foundation model processes time-series RPL data collected at the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Evaluation pipeline for the MOMENT-based IDS. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Effect of window size (stride=16) on MOMENT, showing F1 across attack patterns and attack families together with the corresponding training time. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of window size and stride on MOMENT performance, showing F1 across temporal regimes and window sizes. [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
read the original abstract

AI-based intrusion detection systems (IDS) have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network configurations, and provide insights into the performance of foundation models for attack identification. Specifically, we fine-tune the MOMENT foundation model for multi-class attack identification. Our evaluation is based on a dataset containing RPL-related statistics collected under normal operation and under Blackhole, DIS flooding, Worst Parent, and Local Repair attacks, generated in a Cooja simulation environment. The initial results are promising. The approach achieves attack-detection performance comparable to state-of-the-art methods, while also demonstrating strong performance in distinguishing between different attack types.

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

3 major / 1 minor

Summary. The manuscript explores the use of foundation models for intrusion detection in RPL-based IoT networks. It fine-tunes the MOMENT model on RPL-related statistics collected from Cooja simulations under normal operation and four attack types (Blackhole, DIS flooding, Worst Parent, Local Repair), claiming that the initial results show attack-detection performance comparable to state-of-the-art methods together with strong multi-class distinction between attack types.

Significance. If the performance claims are substantiated with quantitative metrics, baselines, and evidence of generalization beyond simulation, the work could demonstrate the viability of foundation models for RPL-specific IDS in constrained IoT settings and encourage further exploration of transfer learning for protocol-level security.

major comments (3)
  1. [Abstract and §4 (Evaluation)] Abstract and §4 (Evaluation): the central claim that the fine-tuned MOMENT model achieves attack-detection performance 'comparable to state-of-the-art methods' is unsupported because the section supplies no numerical metrics (accuracy, precision, recall, F1, or confusion matrices), no baseline comparisons, no training hyperparameters, and no error analysis.
  2. [§4 (Evaluation)] §4 (Evaluation): the entire performance assessment rests on a Cooja-generated dataset; the manuscript contains no real-hardware validation or analysis of how Cooja's abstractions of radio propagation, timing jitter, and external interference affect RPL DODAG formation and attack traces, which directly undermines the claim of applicability to real IoT networks.
  3. [§3 (Methodology)] §3 (Methodology): the description of the fine-tuning procedure for MOMENT lacks any specification of the loss function, learning-rate schedule, dataset split ratios, handling of class imbalance across the four attacks, or the distinction between binary detection and multi-class identification, preventing assessment of reproducibility or the source of the reported multi-class performance.
minor comments (1)
  1. [Abstract] The abstract uses the phrase 'initial results are promising' without indicating the scope or limitations of the evaluation; replacing it with a more precise statement would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below, agreeing where revisions are needed to strengthen the manuscript and providing clarifications on simulation-based evaluation.

read point-by-point responses

  1. Referee: [Abstract and §4 (Evaluation)] Abstract and §4 (Evaluation): the central claim that the fine-tuned MOMENT model achieves attack-detection performance 'comparable to state-of-the-art methods' is unsupported because the section supplies no numerical metrics (accuracy, precision, recall, F1, or confusion matrices), no baseline comparisons, no training hyperparameters, and no error analysis.

    Authors: We agree that the current version lacks these quantitative elements to support the comparability claim. In the revised manuscript, we will add accuracy, precision, recall, F1-scores, and confusion matrices for both detection and identification tasks. We will include baseline comparisons against traditional ML methods and other IDS approaches, specify all training hyperparameters, and provide error analysis to substantiate the performance claims. revision: yes

  2. Referee: [§4 (Evaluation)] §4 (Evaluation): the entire performance assessment rests on a Cooja-generated dataset; the manuscript contains no real-hardware validation or analysis of how Cooja's abstractions of radio propagation, timing jitter, and external interference affect RPL DODAG formation and attack traces, which directly undermines the claim of applicability to real IoT networks.

    Authors: We acknowledge the evaluation relies exclusively on Cooja simulations, a standard approach in RPL literature for controlled experimentation. We do not claim immediate real-world applicability. In revision, we will expand §4 with an explicit limitations subsection discussing Cooja's modeling of radio propagation, jitter, and interference, and their potential effects on DODAG formation and attack traces. We will frame results as simulation-based insights and identify real-hardware validation as future work. revision: partial

  3. Referee: [§3 (Methodology)] §3 (Methodology): the description of the fine-tuning procedure for MOMENT lacks any specification of the loss function, learning-rate schedule, dataset split ratios, handling of class imbalance across the four attacks, or the distinction between binary detection and multi-class identification, preventing assessment of reproducibility or the source of the reported multi-class performance.

    Authors: We agree these details are necessary for reproducibility. The revised §3 will specify the loss function (cross-entropy), learning-rate schedule, dataset split ratios, class-imbalance handling (via weighted loss), and clearly separate the binary detection task from the multi-class attack-type identification task, including how each contributes to the reported performance. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation on held-out simulation data

full rationale

The paper presents an empirical machine-learning study: fine-tuning the MOMENT foundation model on RPL statistics generated in Cooja under normal and attack conditions, then reporting detection and multi-class identification performance on (presumably held-out) simulation traces. No equations, parameter-fitting steps, or derivation chains are described that would reduce any claimed result to its own inputs by construction. No self-citation load-bearing arguments appear in the provided text. The central performance claims therefore remain independent of the training data in the statistical sense of held-out evaluation, satisfying the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical application study and introduces no free parameters, mathematical axioms, or invented entities beyond the standard use of an existing foundation model and a simulator.

pith-pipeline@v0.9.1-grok · 5670 in / 1096 out tokens · 31839 ms · 2026-06-28T09:30:43.448897+00:00 · methodology

discussion (0)

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Reference graph

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