Evidence map›Paper›PMID 42356750›Full record

ArticleSensors (Basel, Switzerland)2026

An Unsupervised Detection-to-Mitigation Framework for Resource Exhaustion Attacks in 5G/6G Network Slicing.

Ja-Eun Kim, Hye-Yoon Jeong, Jae-Hyun Pi, Myung-Sun Baek, Hyoung-Kyu Song

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ja-Eun KimDepartment of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.
Hye-Yoon JeongDepartment of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.
Jae-Hyun PiDepartment of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.
Myung-Sun BaekDepartment of Artificial Intelligence and Information Technology, Sejong University, Seoul 05006, Republic of Korea.ORCID 0000-0003-3915-9409
Hyoung-Kyu SongDepartment of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.ORCID 0000-0002-3274-4982

Funding

Institute of Information & Communications Technology Planning & Evaluation IITP-2026-RS-2021-II211816Institute of Information & Communications Technology Planning & Evaluation IITP-2026-RS-2023-00254529National Research Foundation of Korea 2020R1A6A1A03038540
6 · The paper itself

Abstract

Massive Internet of Things (IoT) and sensor-network services in 5G/6G systems increasingly rely on network slicing to support large-scale sensing, monitoring, and mission-critical applications. In such sliced infrastructures, Proportional Fair (PF) allocation assigns resources according to slice-reported demands. This reliance on trusted demand reporting makes coexisting slices, including mMTC-based IoT sensor slices, vulnerable to resource exhaustion attacks, where a malicious slice inflates its demand to monopolize shared resources and induce Service Level Agreement (SLA) violations. Existing unsupervised defenses mainly focus on anomaly detection, while the translation of detection results into resource-level mitigation remains insufficiently addressed. To bridge this gap, this paper proposes AutoGuard-Hybrid, an unsupervised detection-to-mitigation framework that combines complementary anomaly detectors with allocation-aware mitigation policies to preserve slice-level service availability. Unlike prior detection-only approaches, AutoGuard-Hybrid converts unsupervised anomaly evidence into allocation-aware demand purification before PF scheduling. Its key design is a closed-loop integration of Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) as spatial and temporal front-end detectors with Adaptive Clipping and a Safety Cap, which translate anomaly scores into demand purification actions. Experiments show that AutoGuard-Hybrid remains comparable to Isolation Forest under Continuous attacks and improves the mean system-wide SLA violation rate by 27.6% under Adaptive Probing attacks. Stage activation analysis further shows that LSTM-AE activations increase from 9.3 under Continuous attacks to 29.4 under Adaptive Probing attacks. Ablation results show that Adaptive Clipping alone reduces the system-wide SLA violation rate by 75.0%, while the full mitigation pipeline achieves an 84.6% total reduction. AutoGuard-Hybrid operates within the 1 ms Transmission Time Interval (TTI) constraint and provides a practical defense framework for next-generation network slicing-enabled IoT and sensor-network services.

Indexed as

5G/6G networksallocation-aware mitigationdetection-to-mitigation frameworkIoT sensor networksmMTC slicenetwork slicing securityresource exhaustion attackslice-level service availabilityunsupervised anomaly detection

Identifiers

PMID42356750
PMCPMC13306978

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.