# SHIELD: Scalable and Holistic Evaluation Framework for ML-Based 5G Jamming Detection

Jiali Xu, Aya Moheddine, Valéria Loscrì, Alessandro Brighente, Mauro Conti

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Jiali Xu, Aya Moheddine, Valéria Loscrì, Alessandro Brighente, Mauro Conti. SHIELD: Scalable and Holistic Evaluation Framework for ML-Based 5G Jamming Detection. 20th International Conference on Availability, Reliability and Security (ARES), Aug 2025, Ghent (BE), Belgium. hal-05055784v2

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# SHIELD: Scalable and Holistic Evaluation
## Framework for ML-Based 5G Jamming Detection

Jiali Xu¹, Aya Moheddine¹, Valéria Loscrì¹, Alessandro Brighente², and Mauro Conti²

¹ Inria Centre at the University of Lille, 59650 Villeneuve d'Ascq, France
{firstname.lastname}@inria.fr

² Dept. of Mathematics, University of Padova, 35131 Padova, Italy
{firstname.lastname}@unipd.it

**Abstract.** Jamming remains a significant threat to 5G network reliability and security, despite extensive research. This work addresses critical scalability and robustness gaps in previous approaches by introducing SHIELD: a scalable, holistic framework to evaluate jamming interference and support Machine Learning (ML)-based detection techniques without relying on costly external hardware. To validate our approach, we developed a realistic 5G testbed featuring a power-modulated jammer, commercial off-the-shelf Android devices, and a Software-Defined Radio (SDR)-based Radio Access Network (RAN). Our experiments demonstrate this setup generates complex interference patterns challenging prior detection methods. SHIELD's novel methodology involves synchronously collecting native logs from User Equipment (UE) and Next-Generation Node B (gNB), capturing a comprehensive view of network behavior. It overcomes existing methods' shortcomings against subtle, long-term interference by employing a robust preprocessing pipeline with interpolation and sliding-window aggregation for multi-layer feature extraction. We assess several lightweight classifiers (Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Random Forest (RForest)) across diverse real-world scenarios. Our evaluation reveals that while existing methods may exceed 90% accuracy in controlled settings, their performance can drop below 70% under varying conditions. In contrast, SHIELD's log-based framework maintains approximately 94% accuracy on unseen data, offering a scalable, cost-effective, and robust solution for large-scale 5G deployments.

**Keywords:** 5G Networks · Wireless Security · Jamming Attack · Pattern Analysis · Anomaly Detection.

## 1 Introduction

Cellular networks form the backbone of modern communication systems, connecting millions of users through voice calls, internet browsing, and various data-intensive applications. These networks rely on seamless Radio Frequency (RF)
signal transmission between mobile devices, such as smartphones and base sta-
tions. The emergence of advanced technologies like 5G has enabled unprece-
dented speed, capacity, and reliability. However, the open and shared nature
of wireless channels brings inherited vulnerabilities [4, 23], including radio jam-
ming interference—a fundamental threat that extensive research has not fully
eliminated.

Jamming, though not a novel concept, remains a critical wireless attack. By
injecting disruptive RF signals on the same frequencies that cellular networks
use, adversaries can degrade performance, increase latency, and cause dropped
calls or total communication failure, effectively leading to denial-of-service sce-
narios. Such disruptions not only frustrate end users but can have serious impli-
cations in sectors dependent on uninterrupted connectivity, such as healthcare,
transportation, and critical infrastructure.

Traditionally, detecting and mitigating jamming attacks has required spe-
cialized hardware, such as spectrum analyzers and advanced signal processing
systems [25, 20], to capture RF fingerprints. These methods can be costly and
lack scalability, making them unsuitable for wide deployment in end devices.
Furthermore, the proposed methodologies in prior works often focus on a sin-
gle network entity, relying on measurements either from the RAN or the UE,
which limits the holistic analysis of system behavior. Additionally, many exist-
ing detection methods [21, 5, 6, 10, 13, 14] primarily address simplistic jamming
profiles, such as constant or reactive jammers. These types of interference of-
ten produce overt and consistent disruptions, making them more amenable to
detection. However, sophisticated adversaries can employ more subtle, power-
modulated jamming strategies that generate less predictable interference pat-
terns. Such complex variants pose significant challenges to conventional tech-
niques, particularly those validated predominantly through simulations, which
may not fully capture the intricacies of real-world 5G operational dynamics and
hardware interactions.

Being aware of these gaps in the current literature, we propose the SHIELD
framework. The main contributions of this work are: (1) We construct a power-
modulated jammer and integrate into a near-realistic 5G setup using commercial
off-the-shelf Android devices and an SDR-driven RAN. We reproduce several de-
tection methods from prior studies in our testbed to demonstrate the challenge of
detecting such complex jamming profiles. Additionally, we collect data directly
from these devices to validate the effectiveness of our subsequent proposals, high-
lighting the limitations of existing techniques under more subtle jamming sce-
narios. (2) We propose an approach that captures features from native UE logs
and gNB traces to remove excessive hardware dependencies. This approach in-
cludes an efficient preprocessing pipeline that preserves crucial signals and filters
out non-jamming fluctuation, providing a scalable and holistic analysis toward
accurate and robust jamming detection. (3) We design an evaluation method
that balances performance with resilience to varying real-world conditions, a di-
mension the literature often overlooks. We illustrate this approach with several
lightweight yet highly accurate ML models, ensuring a broader applicability of
these detection approaches beyond a single, strictly controlled environment.

The remainder of this paper proceeds as follows: In Section 2, we review related work on jamming detection and ML-based anomaly detection to highlight existing challenges and gaps. Section 3 describes our proposed SHIELD framework in detail, including the design of a power-modulated jammer, our cross-layer data collection approach, and the preprocessing pipeline used to extract and aggregate features from native logs. Section 4 presents our experimental setup, evaluation results, and comparative analysis of various ML models. In Section 5, we discuss the implications of our findings, the limitations of the current approach, and potential directions for future work. Finally, Section 6 concludes the paper with a summary of our contributions and insights.

## 2 Related Work

This section reviews the landscape of jamming approaches in wireless communication networks, focusing on 5G environments. It also examines recent jamming analysis and detection techniques developed specifically for 5G systems.

### 2.1 Jamming Attacks in 5G Networks

Jamming attacks have been extensively examined in wireless networks because they can disrupt communication by injecting noise into the same frequencies used by legitimate devices. Exploiting the broadcast nature of wireless media, a jammer can overwhelm RF signals and effectively target the availability of network resources, creating serious security challenges. Such attacks pose risks across a variety of wireless applications, from the Internet of Things (IoT) and wireless sensor networks to vehicular communications and 5G infrastructure.

Jamming strategies can be categorized based on how they interfere with network operations, each requiring distinct detection and mitigation methods [23]. (i) Constant jammers emit signals at a single frequency to continuously block legitimate communication. This tactic is straightforward but power-intensive, making it more easily detected by monitoring uncommonly high interference levels. (ii) Deceptive jammers mimic legitimate network signals, causing nodes to process false information or remain silent; their resemblance to valid traffic makes detection challenging. (iii) Random jammers transmit interference at irregular intervals, improving stealthiness while complicating detection. Finally, (iv) reactive jammers [29] transmit only upon sensing network activity, aligning their interference with legitimate transmissions to save energy and avoid easy identification.

Researchers have investigated these attack types to assess their impact on network performance and to develop targeted countermeasures. Savadatti et al. [28] offered a comprehensive taxonomy of jamming attacks in 5G, classifying them by factors such as jammer characteristics, attack methodology, and security implications. Similarly, a thorough survey in [25] analyzed a wide spectrum of
jamming threats and countermeasures in modern wireless systems, highlighting the unique vulnerabilities introduced by high-frequency bands, massive MIMO (Multiple-Input Multiple-Output) antennas, and network slicing in 5G. Other works have focused on specific attack scenarios. Flores et al. [11] demonstrated that targeted uplink jamming can reduce the throughput of a specific UE by 100% if a jammer exploits resource allocation information. Salehi et al. [27] explored how deep reinforcement learning can enable adaptive jammers to disrupt resource blocks assigned to network slices, indicating that adversaries are increasingly sophisticated.

While existing research has extensively examined various jamming profiles, many studies focus on attacks like constant, deceptive, or reactive jamming. These often induce consistent and easily identifiable network impacts, enabling simpler detection via traditional approaches optimized for such straightforward interference patterns. However, reliance on these simpler jamming models, often evaluated within simulated environments, provides limited insight into the efficacy of detection techniques against more sophisticated, real-world threats (we further demonstrate this in Section 4.2). In practice, network environments are dynamic, and adversaries are likely to employ jammers that create more subtle and complex interference to evade detection. To address this critical gap, our work introduces and evaluates defenses against a power-modulated jammer. This type of jammer, by design, generates unpredictable interference effects through time-varying amplitude patterns. By incorporating this more realistic threat model within a physical testbed, SHIELD aims to develop and validate detection approaches that are robust against such complex, evasive jamming scenarios, thereby highlighting the necessity for evaluations that move beyond simplistic simulated attacks.

## 2.2 Anomaly and Attack Detection in 5G Networks

Given the growing scale and complexity of 5G, anomaly detection is integral to maintaining secure and reliable communications. Various ML approaches have been proposed to detect malicious activities in real-time. Lam et al. [19] introduced a Software-Defined Security (SDS) framework employing Convolutional Neural Network (CNN) to identify anomalous network traffic, achieving a high detection rate for both benign and malicious flows. Rahman et al. [26] discussed the use of diverse ML algorithms—including KNN and K-prototype—for anomaly detection in the inherently stochastic 5G environment. Their work showed that integrated or ensemble techniques can outperform single-method approaches.

Other research efforts have explored deep learning in depth. Doan et al. [8] applied deep neural networks to detect anomalies without degrading 5G performance. Issa et al. [15] demonstrated a deep learning solution for anomaly detection and traffic forecasting in 5G core networks. Meanwhile, Arjoune et al. [4] examined 5G NR vulnerabilities to jamming, offering insights into diverse jamming models and the effectiveness of existing defenses. Alenazi et al. [1] used
a deep-hybrid learning method to identify wireless cyberattacks in 5G, surpassing traditional ML methods in accuracy.

Recent studies have applied specialized ML models to 5G jamming scenarios. Bousalem et al. [7] explored deep learning to detect jamming in vehicular-to-infrastructure (V2I) and vehicular-to-network (V2N) communications, emphasizing the importance of reliable data exchange. Hachimi et al. [13] presented a multi-stage ML-based intrusion detection system for 5G Cloud-RAN, focusing on jamming attack classification while minimizing false negatives. Jere et al. [16] combined supervised learning with Bayesian inference to detect jamming in 5G Non-Standalone (NSA), achieving high accuracy. Arjoune et al. [3] used Hoeffding Decision Trees for real-time jamming detection, enabling rapid training to mitigate attacks effectively. Asemian et al. [6] proposed a robust deep learning architecture for over-the-air detection of smart and barrage jamming, incorporating Discrete Wavelet Transform (DWT) for high-accuracy classification under various Signal-to-Jamming-and-Noise Ratio (SJNR) conditions.

Beyond algorithmic approaches, researchers have investigated the role of specific features and device-side data. Forssell [12] leveraged UE-measured Signal-to-Interference and Noise Ratio (SINR) for both detection and localization, revealing how signal quality metrics can serve as vital inputs for interference identification. Kryszkiewicz et al. [18] emphasized 5G NR Radio Resource Control (RRC) measurement reports to augment jamming detection, demonstrating the importance of device-native capabilities in crafting robust security solutions.

We conclude the following persisting gaps while ML and deep learning techniques have advanced anomaly and jamming detection in 5G: (1) Many studies [32] still focus on overly simplistic jamming profiles that favor detection. (2) Few efforts collect data simultaneously from both the UE and gNB perspectives, creating an incomplete view of network anomalies. (3) Some complex and high-accuracy solutions [20] rely on external hardware to capture physical-layer signals, restricting scalability to the fast-expanding 5G environment. (4) Many existing solutions rely heavily on simulations, raising concerns about their real-world applicability. These limitations highlight the need for a more scalable, holistic, cross-layer approach that leverages practical, system-wide data from both ends of the 5G communication link.

## 3 Proposed Framework: SHIELD

We propose SHIELD (Figure 1) to address challenges that current detection methods fail to overcome: scalability and holiness. We present this framework as an end-to-end solution, that incorporates wireless jamming modules and ML flows for assessing jamming interference on both the UE and gNB in a 5G environment. It includes (1) a controlled wireless jamming implementation that challenges existing defenses and serves as the target in the evaluation pipeline; (2) a cross-end data collection mechanism that merges UE logs and gNB traces to quantify system behavior under jamming; (3) a robust and efficient preprocessing methodology to extract rich contextual information from raw logs; and
(4) a rigorous evaluation methodology that employs a primary dataset for training and initial validation, and a distinct, separately collected dataset—which we term the robustness test set—to measure ML model resilience against new, unseen operating conditions.

<figure><img src="image_1.png" /><figcaption>Fig. 1: Architecture of the SHIELD Framework. The diagram illustrates the end-to-end pipeline, including ① UE devices, gNB, and the power-modulated jammer testbed setup; ② the Log and Trace Collector for synchronized data acquisition from UE and gNB; ③ the configurable Preprocessor with feature extraction, interpolation, and aggregation modules, which feed into the ML module using distinct data flows for training/validation (from the primary dataset) and robustness testing (from the separate test set); and ④ the ML module for model deployment and performance reporting.</figcaption></figure>

## 3.1 Controlled Jamming Interference

We assume an adversary can position a jamming signal emitter between the UE and gNB; use competitive signal power, and interfere with uplink and downlink control channels. Rather than severing connectivity—which would trigger obvious alarms—the adversary aims to degrade performance subtly and prolong interference without immediate detection.

A key element of this framework is a controllable jamming module to impact the system behavior. In this study, we implemented a power-modulated jammer that simulates intentional interference by periodically varying its amplitude.
Unlike constant or reactive jammers, a power-modulated jammer leverages sinu-
soidal amplitude patterns to produce a variable-power noise profile, making its
interference less predictable and more reflective of real-world adaptive jamming
scenarios. Our experimental results demonstrate that this jamming technique
compromises several existing data-driven detection approaches, highlighting the
need for more robust detection methods, as discussed in the following section 4.2.

We design the jammer to operate autonomously without external inputs. It
generates the interference signal based on the following parameters:

- *Sampling Rate* $r_{samp}$: Controls the frequency resolution of the signal, enabling more precise manipulations of jamming characteristics as the rate increases.

- *Slot Length* $L_{slot}$: Defines the interval—expressed in samples—at which the amplitude pattern resets, adding structured periodicity that can mimic pulsed or intermittent jamming scenarios.

- *Amplitude Range* ($min_A, max_A$): Governs the power of the emitted signal, supporting a wide variety of jamming strengths.

- *Pattern Frequency* $f_{pat}$: Determines the rate of sinusoidal oscillations in amplitude, allowing the jammer to vary its power in real time and evade straightforward detection.

The jammer produces complex output signals using Algorithm 1. The jamming signal is structured around slots to generate bursts of interference. Within each slot, the amplitude resets based on an adjustable sinusoidal pattern through various pattern frequency $f_{pat}$ and sampling rate $r_{samp}$ values. This pattern design allows the jammer to fluctuate its power dynamically over time and challenge the static noise filter of wireless systems.

## 3.2 Data Source and Context

Recent work [12] suggests that rich metrics and telemetry embedded in system logs, generated by end devices during regular network operations, can reveal signatures of jamming attacks. In our proposed approach, the detection system is deployed directly on the UE or gNB device itself, utilizing native logs and traces to capture how the system behaves under jamming interference. This choice of deployment allows the system to access data directly from the device, which is readily available and avoids the need for additional probing tools or specialized hardware. Specifically, by mining these native logs, we can enable ML-based jamming detection without requiring extra infrastructure.

This deployment strategy offers several advantages: (1) Improved data ac-
cessibility: By collecting device-level metrics that are available by default, we
eliminate the need for additional probes or external hardware, making the sys-
tem self-contained and easier to deploy in a large-scale, distributed manner.
(2) Multi-layer view of the network: The collected data spans both Media Ac-
cess Control (MAC)-related metrics (e.g., signal quality, Modulation and Coding
Scheme index) and system-level insights (e.g., resource usage reports, schedul-
ing decisions). This cross-layer data provides a comprehensive view of network
**Algorithm 1** Power-Modulated Jamming.

**Require:** Sampling Rate $r_{ samp}$, Slot Length $L_{ slot}$, Amplitude Range ($min_{ A}$, $max_{ A}$), Pattern Frequency $f_{ pat}$

**Ensure:** Jamming Signal $J_{ signal}$

1: Initialize time index $n \leftarrow 0$

2: Compute amplitude modulation factor: $A_{ m} \leftarrow \frac{1}{2}(max_{ A} - min_{ A})$

3: **while** Jamming is active **do**

4: Compute amplitude modulation for current time step:

$$A(n) \leftarrow min_{ A} + A_{ m} \times \left( 1 + \sin \left( \frac{2\pi n f_{ pat}}{r_{ samp}} \right) \right)$$

5: Generate jamming signal:

$$J_{ signal}(n) \leftarrow A(n) \times Noise(n)$$

6: Transmit $J_{ signal}(n)$

7: Increment time index: $n \leftarrow n + 1$

8: **if** $n \bmod L_{ slot} = 0$ **then**

9: Reset amplitude pattern for the next slot

10: **end if**

11: **end while**

behavior under jamming interference, strengthening detection accuracy. (3) End-to-end observation: By monitoring both device endpoints (the UE and gNB), we gain a holistic view of how jamming impacts normal network operations. This end-to-end perspective enhances the detection of subtle anomalies in the communication process. (4) Real-time detection capability: Since the detection system relies solely on existing device logs, it can function autonomously, enabling online jamming detection. This makes it feasible to run self-contained algorithms (such as TinyML [9]) on the UE and gNB, which simplifies deployment across multiple devices and facilitates collaborative jamming detection.

We adopt different traffic profiles while capturing this data. First, we use the iperf3 utility at configurable rates to create controlled, replicable traffic that simulates common use cases (e.g., streaming video). This profile provides stable performance under standard conditions and minimizes non-jamming factors, which helps isolate the jammer's impact. Second, we run actual real-world applications such as online gaming or video streaming on the UE to reflect realistic 5G usage. This additional context broadens our analysis and ensures that the dataset covers diverse traffic patterns beyond the scripted tests.

## 3.3 Preprocessing Methodology

A robust preprocessing strategy is essential for any ML pipeline. Our contribution includes a methodology specifically tailored for 5G logs and traces that enriches raw information to ensure reliable jamming detection under diverse real-
world conditions. It contains steps that extract informative features, bridge gaps in temporal data, and construct representative aggregated samples.

**Feature extraction** We start by parsing text-based log messages, which often arrive in key-value structures with embedded tags. We apply adaptable regular expression patterns to detect explicit measurements (e.g., signal strength and quality) in log messages. We also include contextual system-level signals derived from logging behavior (e.g., the frequency of repeated errors, the time gaps between critical events).

Extracted features must show sufficient variation and relevance. We discard those that stay constant, appear too rarely, or lack meaningful insights for classification. We can further customize parsing rules to match the log formats of each manufacturer. A consistent feature mapping across heterogeneous devices ensures that our pipeline remains portable and scales to multi-vendor deployments.

**Interpolation** Two significant challenges arise when converting features into unified time-series data. (1) Different log types have unique logging rates, leading to uneven timestamps or long gaps for certain metrics. As illustrated in Figure 2, Reference Signal Receiving Power (RSRP) measurements in Android logs appear irregularly, causing missing entries in our consolidated dataset. (2) Some traces include invalid entries due to unpredictable hardware malfunctions that are unrelated to jamming interference.

We address these issues with an interpolation-based data cleaning process. We focus on two approaches: (i) forward filling, which propagates the last valid reading until a new data point is observed, and (ii) polynomial interpolation (in particular, the Lagrange method), which fits an n-th degree polynomial to neighboring samples for a smoother transition. Forward filling works best for categorical features (e.g., network mode or carrier band) that do not vary continuously. Polynomial fitting is more effective for quantitative metrics to produce smoother time-series data (as shown in Figure 2). By filling these gaps consistently, we ensure resilience of our subsequent ML models to such randomness.

**Aggregation** Jamming detection often benefits from capturing temporal patterns, as a single instant of data might not reflect deeper trends. Studies such as [16] found that sequential time-series based Recurrent Neural Network (RNN)-type models (e.g., Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU)) outperform purely instantaneous classifiers in jamming detection, but those models come with higher computational costs. To achieve a balance between complexity and accuracy, we use a sliding window aggregation stack. The window buffer size can be tuned to meet different objectives: a larger window captures extended temporal trends but risks slower detection, while a smaller window provides faster reactions and higher sensitivity but less contextual depth.

Within each window, we compute statistical metrics—mean, standard deviation, median, amplitude, skewness, and kurtosis—across the chosen features.
<figure><img src="image_2.png" /><figcaption>Fig. 2: Comparison of interpolation techniques for handling missing/invalid data in log entries. The plot shows raw discrete RSRP measurements from Android logs alongside the output of forward filling and polynomial interpolation (degree=2) over a one-minute interval, illustrating how interpolation creates a continuous time-series.</figcaption></figure>

These metrics highlight persistent patterns rather than instantaneous values. This design helps filter out noise from environmental factors and focus on identifying meaningful interference patterns.

## 3.4 ML Assessment on Robustness

An often-overlooked aspect in anomaly detection is ensuring resilience when real-world conditions deviate from those in lab experiments. Many studies apply a train-test split to a single, homogeneously collected dataset. This often results in overlapping feature distributions between training and testing subsets, and leads to overly optimistic performance estimates (e.g., test accuracies nearing 99%) that may not hold when models are deployed in production environments. Such approaches often lack a clear justification for the distinction between their training and test sets, potentially risking data leakage and yielding models that are not robust to real-world variability.

To address this gap and ensure a more rigorous assessment of generalization, SHIELD employs a two-data-collection strategy. First, we collected a primary dataset under a consistent set of experimental conditions. This primary dataset is partitioned into a training set and a validation set for hyperparameter tuning. Second, we meticulously collected an entirely separate dataset, termed the robustness test set, under demonstrably different network conditions (e.g., varying UE-gNB distances, presence of obstacles, and operating temperature). As we demonstrate with Kernel Density Estimation (KDE) plots in Section 4.1 (Figure 4), the feature distributions in this robustness test set differ significantly
from those in the primary dataset, confirming it represents a genuinely unseen
and more challenging evaluation scenario.

4 Experiment and Results

We evaluated our proposed framework in a quasi-realistic 5G testbed, comprising an open-source 5G core (Open5GS [24]) and a software-emulated RAN running on the srsRAN [30] stack, and a bladeRF 2.0 micro SDR. A OnePlus Nord 2T 5G device served as the UE and was situated close to the gNB within a range from 10 to 15 meters. Its logs were collected via Android Debug Bridge (ADB). To introduce interference, we employed a power-modulated jammer with 1 meter close to the gNB—running on another bladeRF 2.0 micro and transmitting at power levels equivalent to the gNB—that varied its transmit amplitude over time. This setup was hosted in an nonanechoic indoor environment.

To simulate traffic, we ran iperf3 in UDP mode at 10 Mbps to approximate scenarios such as video streaming or other bandwidth-intensive applications. We also collected subsets in which the UE streamed live Youtube content to incorporate a real-world scenario. Figure 3 illustrates key iperf3 performance metrics—bandwidth, jitter, and packet loss—captured over time. Around the midpoint of the measurement period, when the jammer becomes active, the bandwidth begins to decrease, while both jitter and packet loss exhibit noticeable increases. These trends clearly demonstrate the interference introduced by the power-modulated attack.

<figure><img src="image_3.png" /><figcaption>Fig. 3: Impact of Power-Modulated Jamming on Key Performance Indicators (KPIs) from iperf3. The graph shows bandwidth, jitter, and packet loss rate over time, with the vertical dashed line indicating the activation of the power-modulated jammer, demonstrating its negative effect on network performance.</figcaption></figure>
## 4.1 Data Description

We collected logs and traces from both the UE and gNB while toggling between normal operation and jamming interference. We recorded timestamps at the start and end of jamming interference to facilitate accurate dataset labeling, 0 (normal) or 1 (jammed). For class balancing, we enabled the jammer halfway through each session, allowing the normal and jammed segments to be comparable in duration.

On the UE side, we collected timestamped log entries from the radio buffer. These raw logs contained significant redundancy and irrelevant information related to jamming. To enhance relevance, we applied filters that specifically focused on signal strength reports—RSRP, Reference Signal Received Quality (RSRQ), and SINR—which previous studies [12, 18] have demonstrated to be effective for jamming detection. Additionally, we selected AT command records associated with modem operations, including ERFTX for setting transmit power, ECSQ for requesting signal quality, and ETHERMAL for reading thermal reports.

The gNB traces contained rich information including signal power, throughput, and packet transmission metrics (e.g., Physical Uplink Shared Channel (PUSCH), Physical Uplink Control Channel (PUCCH), bit rates, packet success/failure counts). We initially referenced [33], which offers a comprehensive set of features for analyzing network performance in both normal and attack scenarios. However, its complexity and wide range of features introduced excessive noise, obscuring the effects of specific attack patterns. To enhance cross-scenario performance, we refined our dataset by selecting a subset of features most relevant to jamming detection. Specifically, we removed features such as mean values that are susceptible to fluctuations from unexpected network variations while preserving informative indicators that effectively capture jamming-induced anomalies.

As we described in previous section 3.3, two primary data challenges exist: irregular time intervals in the UE logs and a high volume of missing values in the gNB logs. To address these issues, we first pivoted the data and then applied Lagrange polynomial interpolation, resampling each dataset to a uniform 200 ms interval. Next, we used sliding-window aggregation (e.g., mean, standard deviation) to capture contextual trends indicative of jamming events. After discarding uninformative features, we obtained two balanced datasets for binary classification:

- UE Dataset: 6,259 samples (3,202 normal, 3,057 jammed) and 72 features.

- gNB Dataset: 6,958 samples (3,491 normal, 3,467 jammed) and 66 features.

Finally, we analyzed the feature distributions within our primary dataset and our separate test set using KDE. This analysis aimed to assess how individual features and feature pairs differentiate between normal and jammed conditions, and critically, to confirm the distributional differences between the two sets. We selected the top four features from each data source (UE and gNB) based on feature importance rankings from our optimal RForest model. As shown in Figure 4a and 4c for the training portion of our primary dataset, these selected
features or feature pairs display significant shifts under interference, indicating the jammer's distinct impact on system behavior within that initial collection context. More importantly, Figure 4b and 4d illustrates the KDE plots for these same features from our test set. A comparison reveals notably different underlying distributions for these features. Consequently, evaluating models against this separate dataset provides a more rigorous test of cross-scenario generalization.

<figure><img src="image_4.png" /><figcaption>Fig. 4: KDE analysis comparing the distributions of top-ranking features. Subfigures (a) and (c) display feature distributions from our primary dataset for UE and gNB respectively. Subfigures (b) and (d) display distributions of the same features from the separate robustness test set for UE and gNB respectively. The distinct distributions in (b) and (d) compared to (a) and (c) highlight the different operating conditions, crucial for evaluating model generalization.</figcaption></figure>
## 4.2 Benchmarking and Limitations of Existing Detection Methods

To benchmark SHIELD and illustrate the challenges posed by our power-modulated jamming scenario, we evaluated several jamming detection methods from existing literature [32, 10, 5, 31, 14]. These particular works were selected for multiple reasons: firstly, they represent common and lightweight ML classifiers frequently adopted for jamming or anomaly detection in wireless networks; secondly, they utilize input features similar to those considered foundational in many detection systems, such as packet delivery ratio (PDR), bad packet ratio (BPR), and basic signal strength metrics (Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR)) ; and thirdly, assessing their performance with their originally proposed feature sets and without our proposed preprocessing pipeline allows for a clearer understanding of their baseline resilience. Table 1 summarizes these approaches, their reported accuracies in prior work, and their performance on our primary dataset’s validation split and subsequently on our separate robustness test set. Our primary goal in this comparative analysis was to highlight the performance gap that can emerge when methods validated in controlled or simulated environments confront more complex, real-world interference patterns. Consistent with SHIELD’s design philosophy to avoid deployment overhead, we excluded approaches requiring specialized hardware or external IQ sampling, such as RF fingerprinting [20].

<table><caption>Table 1: Comparative analysis of state-of-the-art jamming detection methods. Reported accuracies in the original studies vs. performance on our validation split and our separate robustness test set.</caption><thead><tr><td>Method</td><td>Input Features</td><td>Reported Accuracy</td><td>Validation Accuracy</td><td>Test Accuracy</td></tr></thead><tbody><tr><td>SVM</td><td>PDR, Packet Counts</td><td>95% [32], 98.55% [31]</td><td>73.48%</td><td>36.49%</td></tr><tr><td>RForest</td><td>PDR, BPR, RSSI</td><td>81% [10], 96.6% [5], 98.61% [31]</td><td>66.67%</td><td>53.65%</td></tr><tr><td>Hybrid-Tree</td><td>PDR, BPR, SNR, RSSI</td><td>99.5% [14]</td><td>76.51%</td><td>70.91%</td></tr></tbody></table>

- SVM-based Approach: Abhishek et al. [32] proposed a low-power SVM classifier operating on cumulative PDR and counts of successful or erroneous packets (Packet Counts) over 20-second intervals. Their ns-3 simulation on urban mobility scenario reported over 95% detection accuracy, even when jamming affected only 20%–40% of transmission time. When we evaluated this approach on our dataset, the accuracy dropped to 73.48%, with a notable imbalance between precision (100%) and recall (48.53%). This indicates a strong bias toward predicting attacks and overlooking normal traffic. When evaluated on our test set, the ac-
accuracy declined drastically to 36%, suggesting the SVM was unable to generalize
beyond the conditions it saw in training.

- *RForest Methods:* Multiple works in the literature [10, 5, 31] adopt RForest
classifiers using metrics such as PDR, BPR, RSSI, and SNR. While some papers
briefly mention extended features, they often rely on instantaneous values rather
than time-cumulative or aggregated metrics. Reported accuracies ranged from
81% to nearly 98% in simulations using ns-3 [10] or Matlab [31]. However, our
experiments under realistic conditions resulted in 66.67% validation accuracy,
which dropped to 53.65% on the test scenario. This shortfall highlights the chal-
lenge of detecting subtle, power-modulated attacks that differ from the jamming
profiles in simulation.

- *Hybrid-Tree Classifier:* Hone et al. [14] designed a hybrid tree-based design that combines decision trees with isolation trees to better detect unseen anomalies. This method featured fine-grained isolation trees on decision tree leafs to further classify false-negative entries. They used data from C-band modems and jamming source from an Agilent E4438C signal generator, achieving near-perfect 99.5% detection in controlled tests. They further tested robustness using the open WSN-DS dataset and reached 83.9% accuracy. However, their accuracy dropped to 76.51% on our validation split and 70.91% on the test scenario. Although this approach was more robust than other classifiers, it still fell substantially short of its originally reported performance.

## 4.3 Evaluation of the Proposed ML Pipeline

The results from previous subsection suggest an insufficient performance of ex-
isting jamming detection. To evaluate our proposed preprocessing pipeline, we
incorporated four ML classifiers that are common, computationally efficient and
scalable even to edge devices—SVM, KNN, GB, and RForest—to demonstrate
the viability of data-driven jamming detection.

We trained four classifiers using grid search and 10-fold cross-validation to tune hyperparameters. Table 2 summarizes these optimal configurations and the resulting F1-scores across both the UE and gNB datasets. For each pipeline, we employed different scoring strategies, as illustrated in Figure 5a and 5c. Overall, all four models converged successfully and reached roughly 90% validation accuracy, though their performance varied between the UE and gNB datasets. The gNB dataset appeared more sensitive to jamming signals, enabling better detection, while the UE dataset introduced slightly more noise. Notably, SVM tended to excel at recall, minimizing false negatives where jammed states are flagged as normal.

To rigorously assess generalization to new conditions, we further tested the
models on our separate robustness test set. As demonstrated by the KDE plots
in Figure 4b and 4d, the feature distributions in this set exhibit significant shifts
compared to the primary training data. Consequently, when evaluated on this
genuinely unseen data, the performance of our trained models typically dropped
relative to their scores on the validation split. The results from Figure 5b and 5d
highlights the challenge of maintaining detection accuracy outside the initial operating conditions:

- *SVM* showed substantial performance declines under unfamiliar conditions but maintained strong recall score, suggesting it was prone to predict anomalies at the risk of false positives. The linear kernel's decision boundaries struggled to capture the intersection of the two classes, limiting its generalization. Nonetheless, this propensity to over-predict anomalies implies that its negative predictions can be quite reliable, making SVM a competitive candidate for normal-case prediction in a model-stacking framework.

- *KNN* performed reliably in controlled settings and achieved reasonable classification accuracy. However, its performance dropped significantly when faced with varying conditions, reflecting the limitations of a distance-based approach that may be too simplistic for handling complex, context-dependent jamming patterns. Despite these shortcomings, KNN remains attractive for certain applications because of its low computational cost and ease of retraining.

- *GB and RForest* consistently demonstrated strong performance across both UE and gNB datasets, and sometimes even improved on the test set. GB favored higher precision over recall, occasionally overlooking some jamming events while reducing false positives. In contrast, RForest struck a more even balance among these metrics. Both models appeared less vulnerable to environmental changes and maintained robust performance in sophisticated operational contexts.

In general, the experimental results confirm that all these models achieved reasonable detection performance, revealing specific strengths and shortcomings of each model. Meanwhile, tree-based methods (GB and RForest) delivered outstanding robustness to evolving conditions.

# 5 Discussion

Our evaluation demonstrates that a carefully designed data pipeline—one that synchronizes UE logs and gNB traces, applies interpolation and aggregation, and utilizes robust ML algorithms—can effectively detect jamming in quasi-realistic 5G deployments. Previous research proposed similar detection approach basing on same algorithms. Our findings show that without a holistic pre-analysis of system metrics, these established models (e.g., SVM, RForest) cannot consistently detect a power-modulated jammer. By contrast, our pipeline, which leverages native logs from both ends of the link and employs tailored preprocessing, bridges this gap and maintains reasonable performance across different environmental scenarios. These results suggest that even basic ML algorithms gain considerable robustness when trained with a well-designed feature set and cleaning process. Moreover, many prior works [9] have discussed the feasibility of deploying such low-cost algorithms on edge devices (e.g., TinyML), enabling real-time, on-device predictions consistent with the broader trend toward online detection.

In addition, our cross-scenario evaluation demonstrates that on-device logs inherently provide rich information for jamming detection at scale. This scala-
<figure><img src="image_5.png" /><figcaption>Fig. 5: Accuracy, precision, f1, recall and roc-auc scores for each model. Subfigures (a) and (c) show performance on the validation split of the primary UE and gNB datasets, respectively. Subfigures (b) and (d) show performance on the separate robustness test set for UE and gNB data, respectively, to ensure robustness assessment against unseen conditions.</figcaption></figure>

bility aspect addresses a known drawback in prior works, where solutions either hinge on external hardware or rely on limited simulation environments. Admittedly, RF-based jamming analysis—such as building RF fingerprinting [20]—can provide more precise interference profiles. However, these approaches require specialized probes and produce vast amounts of raw IQ data, which can be impractical and costly to manage over large or heterogeneous networks. In contrast, our native log-based strategy delivers a more affordable, easily deployable option. While we have not benchmarked our solution directly against hardware-based probes, our results confirm that our proposed pipeline can provide high and robust detection rates. Therefore, we believe the on-device anomaly detection is the future trend of security solutions, especially in expanding 5G and IoT ecosystems where budget constraints and complexity often limit hardware installation.

## 5.1 Limitations and future directions

**Limited Scope of Diversity and Single Jamming Modality** While the SHIELD framework itself aims to be general, the current evaluation remains narrow. Although we presented a quasi-realistic 5G environment to validate our
<table><caption>Table 2: Hyperparameter tuning search space and optimal configurations for each ML model. The optimal settings, determined by maximizing the F1-score during 10-fold cross-validation on the training split of the primary UE (*) and gNB (+) datasets, are presented.</caption><thead><tr><th>Model</th><th>Hyperparameter</th><th>Values</th><th>F1-score (UE)</th><th>F1-score (gNB)</th></tr></thead><tbody><tr><td rowspan="5">SVM</td><td>C</td><td><b>0.01</b><sup>*+</sup>, 0.1, 10, 100</td><td rowspan="5">0.9147</td><td rowspan="5">0.9479</td></tr><tr><td>kernel</td><td><b>linear</b><sup>*+</sup>, rbf, poly, sigmoid</td></tr><tr><td>gamma</td><td>scale, auto, 0.01, 0.1, 0.5, <b>1</b><sup>*+</sup></td></tr><tr><td>degree</td><td>2, 3, 4, <b>5</b><sup>*+</sup></td></tr><tr><td>tol</td><td>1e-5, <b>1e-4</b><sup>*+</sup>, 1e-3</td></tr><tr><td rowspan="4">k-NN</td><td>n_neighbors</td><td>3, <b>5</b><sup>+</sup>, 7, <b>9</b><sup>*</sup>, 11, 15, 20</td><td rowspan="4">0.8475</td><td rowspan="4">0.9291</td></tr><tr><td>weights</td><td><b>uniform</b><sup>*</sup>, <b>distance</b><sup>+</sup></td></tr><tr><td>algorithm</td><td><b>auto</b><sup>+</sup>, <b>ball_tree</b><sup>*</sup>, kd_tree, brute</td></tr><tr><td>p</td><td><b>1</b><sup>*+</sup>, 1.5, 2</td></tr><tr><td rowspan="7">GB</td><td>n_estimators</td><td><b>50</b><sup>*</sup>, 100, 200, <b>300</b><sup>+</sup>, 500</td><td rowspan="7">0.9229</td><td rowspan="7">0.9326</td></tr><tr><td>learning_rate</td><td>1e-3, 0.01, 0.05, <b>0.1</b><sup>+</sup>, <b>0.2</b><sup>*</sup>, 0.3</td></tr><tr><td>minSamples_split</td><td><b>2</b><sup>*</sup>, 5, <b>10</b><sup>+</sup></td></tr><tr><td>minSamples_leaf</td><td>1, <b>2</b><sup>*+</sup>, 5, 10</td></tr><tr><td>max_depth</td><td><b>3</b><sup>+</sup>, <b>5</b><sup>*</sup>, 10, 20</td></tr><tr><td>max_features</td><td><b>sqrt</b><sup>*</sup>, <b>log2</b><sup>+</sup>, None</td></tr><tr><td>subsample</td><td><b>0.5</b><sup>*+</sup>, 0.7, 1.0</td></tr><tr><td rowspan="6">RForest</td><td>n_estimators</td><td>50, 100, <b>200</b><sup>*</sup>, 300, <b>500</b><sup>+</sup></td><td rowspan="6">0.9107</td><td rowspan="6">0.9291</td></tr><tr><td>minSamples_split</td><td><b>2</b><sup>+</sup>, <b>5</b><sup>*</sup>, 10</td></tr><tr><td>minSamples_leaf</td><td>1, <b>2</b><sup>+</sup>, <b>5</b><sup>*</sup>, 10</td></tr><tr><td>max_depth</td><td>10, <b>20</b><sup>*</sup>, 50, <b>None</b><sup>+</sup></td></tr><tr><td>max_features</td><td><b>sqrt</b><sup>+</sup>, <b>log2</b><sup>*</sup>, None</td></tr><tr><td>bootstrap</td><td><b>True</b><sup>*+</sup>, False</td></tr></tbody></table>

framework, the testbed relied heavily on one power-modulated jamming profile, one specific SDR platform, a single phone model, and a single site setup.

We justified by theory and comparative analysis that power-modulated jammers, which subtly degrade performance over time, can be more challenging to detect than simpler constant or reactive jammers. Successfully detecting such
a stealthy attack illustrates our framework’s potential. Moreover, because our
data pipeline does not rely on any unique jammer characteristics, it can adapt
to other jamming profiles with minimal effort.

Future work should incorporate more diverse hardware (including different chipset vendors or OS variants, and multiple SDR/RF front ends), various sophisticated deployment scenarios, and more complex jamming strategies-ranging from reactive and random attacks to AI-aided adaptive interference. Broader coverage of devices, network conditions, and interference patterns would validate the framework’s generalizability and ensure that it remains robust under heterogeneous 5G deployments.

**Fine-grained Detection and Mitigation** Our present approach primarily identifies whether a jamming attack is active, without distinguishing its intensity or specific interference patterns. More detailed detection strategies could provide insights such as jammer power levels or temporal patterns of interference. This information would be especially valuable for refined countermeasures, such as altering scheduling or power-control parameters on the gNB side. Future work could integrate jamming localization or self-healing network responses to maximize resilience.

**Advanced Techniques on Jamming Analysis** In this work, we focused on four relatively simple ML algorithms, mainly to illustrate how systematic feature engineering and aggregation can boost performance. However, jamming analysis extends well beyond these methods. Model stacking, adaptive or online learning, and more extensive feature engineering could further advance detection reliability and resilience against evolving jamming threats.

Large-scale data collection represents a major availability concern for researchers in the wireless security domain. Running real-device experiments, especially in industrial settings, demands substantial resources, and many devices impose strict constraints on data capture rates (e.g., Teltonika RUTX50 5G Router yields stably one log entry every five seconds). This limitation reduces the amount of high-resolution data, complicating efforts to train complex models such as deep neural networks. Efficient data augmentation techniques offer an appealing solution to address data scarcity. Recent studies [17, 2] highlight how generative models can extend existing datasets and improve anomaly detection generalizability.

Opportunities also remain for exploring advanced detection (e.g. RNN [16]) and mitigation strategies that incorporate richer contextual information. Rather than deploying separate models on individual network components, future efforts can aggregate predictions from multiple entities to improve detection reliability. Further mitigation options, such as pinpointing the jammer’s location, could be integrated into post-detection actions to increase overall network resilience. Reinforcement learning techniques, similar to those proposed by Mowla et al. [22], could dynamically adjust responses based on ongoing detection results, further strengthening the system against evolving jamming threats.
# 6 Conclusion

This work introduced SHIELD, an end-to-end framework designed to evaluate jamming interference in 5G networks using native logs from both the UE and the gNB. Departing from hardware-heavy solutions that rely on specialized RF probes, SHIELD harnessed existing device-side resources, which offered a scalable, cost-effective, and versatile alternative for real-world deployment. Through synchronized data collection, advanced preprocessing (e.g., interpolation, aggregation), and robust ML assessment pipeline, we demonstrated reliable jamming detection across diverse conditions.

By examining both normal and power-modulated jamming scenarios, we highlighted the importance of holistic, cross-layer data and rigorous validation across varying network environments. Our results showed that different ML models excelled in distinct ways, emphasizing how careful feature engineering and pipeline design could significantly enhance detection accuracy and system resilience. Additionally, we underscored how real-time log-based techniques could reduce overhead and speed up deployment timelines relative to more equipment-intensive detection schemes.

Looking ahead, we plan to extend SHIELD's capabilities through online learning, model stacking, and transfer learning methods. These enhancements can further adapt detection pipelines to shifting traffic profiles and evolving jamming strategies in an ever-changing 5G landscape. Another priority involves integrating SHIELD into existing network management and security systems, aiming for unified, real-time defenses that can proactively respond to suspicious patterns. By uniting lightweight, log-centered data collection with advanced analytics, SHIELD seeks to raise security standards and build greater resilience against jamming threats in 5G and next-generation wireless networks.

**Acknowledgments.** This research was made possible with support from the Horizon Europe research and innovation program of the European Union, under grant agreement number 101092912 (project MLSysOps).

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