arXiv subjects

Al Nahian Bin Emran

Publications and source records attributed to Al Nahian Bin Emran.

15 recordsLinked to original sources

arXiv2026

Controller-Only False Confirmation in Passive RF UAV Link Detection

Passive radio frequency (RF) sensing is widely used for counter-unmanned-aerial-vehicle (counter-UAV) detection. Existing studies commonly report high accuracy against background RF or WiFi/Bluetooth interference, but rarely isolate controller-only operation without a linked aircraft. We present a dual-band software-defined radio (SDR) measurement study with ambient, controller-only, and linked states for three commercial UAV platforms (DJI Phantom 3 4K, Hubsan H501S, DJI Mavic Mini). Two USRP B210 receivers simultaneously scan eight 2.4 GHz and twelve 5.8 GHz observation windows across twenty rounds. We first train an energy-based detector using linked and ambient scans only. At four ranked concurrent dwell steps (8 s), it achieves 0.992 linked-versus-ambient balanced accuracy but false-confirms 30 out of 60 controller-only scans (FCRctrl = 0.500). We then include controller-only scans in training and use confirm, reject, and defer outputs under an explicit controller-only false-confirmation-rate (FCR) constraint. For the pooled all-platform analysis, the constraint reduces observed FCRctrl from 0.350 to 0.050, while confirm-linked TPR decreases from 0.900 to 0.400 and 42.1% of scans are deferred. The corresponding compact scan performs substantially better for Hubsan and Mavic than for Phantom. A hardware-in-loop experiment reduces measured wall-clock time from 82.9 s to 28.9 s. These measurements show that linked-versus-background accuracy does not measure controller-only false confirmation and that this error should be reported separately.

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arXiv2026

Battlefield 5G: Dual-PKI and TPM-Based UE Attestation for Tactical 5G Standalone Networks

The standardized 5G Authentication and Key Agreement (5G-AKA) authenticates a subscriber credential stored on a Universal Subscriber Identity Model (USIM) but does not authenticate the physical device that holds that credential or verify its boot state. This gap is significant in tactical 5G deployments, where user equipment may be captured, modified, returned to service, or used with transplanted subscriber credentials. We present Battlefield 5G, a pre-authentication framework for 5G Standalone networks that combines dual X.509 device-certificate checks with Trusted Platform Module (TPM) -based boot attestation before standard registration is accepted. The design places an outer certificate challenge on the 5G base-station called gNB, an independent inner certificate challenge on the Access and Mobility Management Function (AMF) in the 5G core network, and a TPM PCR (Platform Configuration Register) quote verified by an attestation proxy on the 5G core network side. A gNodeB (gNB) side Radio Resource Control (RRC) forwarding gate and an AMF-side save-and-replay mechanism enable multi-round certificate and attestation challenge-response exchanges to be inserted into the registration path without modifying any 3GPP Non-Access Stratum (NAS) message structures or adding new NAS message types. We implement these capabilities by extending the Radio Access Network of the Software Radio System (srsRAN), gNB, User Equipment of the Software Radio System (srsUE) and Open5GS in a B210-based Universal Radio Peripheral (USRP) testbed with a hardware TPM 2.0 in the UE. The prototype blocks SIM-transplant, rogue-certificate, firmware-tampering, and replay attacks. Across six trials, Battlefield 5G increases average onboarding latency from 1886 ms to 2260 ms, adding 373.4 ms of pre-authentication overhead while preserving standard 5G-AKA, security mode, and packet data unit (PDU) session procedures.

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arXiv2026

SoK: Analysis of Privacy Risks and Mitigation in Online Propaganda Detection through the PROMPT Framework

Online propaganda detection pipelines expose measurable privacy risks at multiple stages including data collection, feature extraction, and model inference. We conduct a structured analysis of $162$ peer-reviewed studies and formalize the problem using the Propaganda Risk Online Mitigation and Privacy-preserving Tactics (PROMPT) framework. PROMPT models risks $R$ and mitigation strategies $S$ through a mapping $M: R\to S$ guided by a utility function $α\cdot \mathrm{PrivacyGain}(s_j) - β\cdot \mathrm{PerfLoss}(s_j) - γ\cdot \mathrm{Cost}(s_j)$, with tunable $(α,β,γ)$ enabling stakeholders to balance privacy, accuracy, and deployment costs. To assess practical adoption, we introduce a compliance score that quantifies the alignment of existing methods with GDPR, CCPA etc. requirements. Our evaluation shows that many widely used pipelines remain non-compliant, particularly in metadata handling and user-level aggregation. We further present empirical fine-tuning experiments on transformer-based encoders and decoders under synthetic perturbation, demonstrating a monotonic privacy-utility trade-off: with $q = 0.05$ performance decreased by 1-2% F$_1$, while at $q = 0.20$ the reduction reached 13-14%. These results establish quantitative baselines for privacy costs in propaganda detection. Our contributions include a formal risk-to-defense mapping, a compliance-oriented auditing metric, and experimental evidence of privacy-performance trade-offs, providing a technical foundation for building regulation-compliant and privacy-aware detection systems.

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arXiv2026

Availability Attacks Without an Adversary: Evidence from Enterprise LANs

Denial-of-Service (DoS) conditions in enterprise networks are commonly attributed to malicious actors. However, availability can also be compromised by benign non-malicious insider behavior. This paper presents an empirical study of a production enterprise LAN that demonstrates how routine docking and undocking of user endpoints repeatedly trigger rapid recalculations of the control plane of the Rapid Spanning Tree Protocol (RSTP) [1]. Although protocol-compliant and nonmalicious, these events introduce transient forwarding disruptions of approximately 2-4 seconds duration that degrade realtime streaming (voice and video) services while remaining largely undetected by conventional security monitoring. We map this phenomenon to the NIST and MITRE insider threat frameworks, characterizing it as an unintentional insider-driven availability breach, and demonstrate that explicit edge-port configuration effectively mitigates the condition without compromising loop prevention

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arXiv2025

Quantitative Analysis of UAV Intrusion Mitigation for Border Security in 5G with LEO Backhaul Impairments

Uncooperative unmanned aerial vehicles (UAVs) pose emerging threats to critical infrastructure and border protection by operating as rogue user equipment (UE) within cellular networks, consuming resources, creating interference, and potentially violating restricted airspaces. This paper presents minimal features of the operating space, yet an end-to-end simulation framework to analyze detect-to-mitigate latency of such intrusions in a hybrid terrestrial-non-terrestrial (LEO satellite) 5G system. The system model includes terrestrial gNBs, satellite backhaul (with stochastic outages), and a detection logic (triggered by handover instability and signal quality variance). A lockdown mechanism is invoked upon detection, with optional local fallback to cap mitigation delays. Monte Carlo sweeps across UAV altitudes, speeds, and satellite outage rates yield several insights. First, satellite backhaul outages can cause arbitrarily long mitigation delays, yet, to meet fallback deadlines, they need to be effectively bounded. Second, while handover instability was hypothesized, our results show that extra handovers have a negligible effect within the range of parameters we considered. The main benefit of resilience from fallback comes from the delay in limiting mitigation. Third, patrol UEs experience negligible collateral impact, with handover rates close to terrestrial baselines. Stress scenarios further highlight that fallback is indispensable in preventing extreme control-plane and physical security vulnerabilities: Without fallback, prolonged outages in the satellite backhaul delay lockdown commands, allowing rogue UAVs to linger inside restricted corridors for several seconds longer. These results underscore the importance of complementing non-terrestrial links with local control to ensure robust and timely response against uncooperative UAV intrusions.

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arXiv2025

TPM-Based Continuous Remote Attestation and Integrity Verification for 5G VNFs on Kubernetes

In the rapidly evolving landscape of 5G technology, the adoption of cloud-based infrastructure for the deployment of 5G services has become increasingly common. Using a service-based architecture, critical 5G components, such as the Access and Mobility Management Function (AMF), Session Management Function (SMF), and User Plane Function (UPF), now run as containerized pods on Kubernetes clusters. Although this approach improves scalability, flexibility, and resilience, it also introduces new security challenges, particularly to ensure the integrity and trustworthiness of these components. Current 5G security specifications (for example, 3GPP TS 33.501) focus on communication security and assume that network functions remain trustworthy after authentication, consequently lacking mechanisms to continuously validate the integrity of NVFs at runtime. To close this gap, and to align with Zero Trust principles of 'never trust, always verify', we present a TPM 2.0-based continuous remote attestation solution for core 5G components deployed on Kubernetes. Our approach uses the Linux Integrity Measurement Architecture (IMA) and a Trusted Platform Module (TPM) to provide hardware-based runtime validation. We integrate the open-source Keylime framework with a custom IMA template that isolates pod-level measurements, allowing per-pod integrity verification. A prototype on a k3s cluster (consisting of 1 master, 2 worker nodes) was implemented to attest to core functions, including AMF, SMF and UPF. The experimental results show that the system detects unauthorized modifications in real time, labels each pod's trust state, and generates detailed audit logs. This work provides hardware-based continuous attestation for cloud native and edge deployments, strengthening the resilience of 5G as critical infrastructure in multi-vendor and mission-critical scenarios of 5G.

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arXiv2025

gNB-based Local Breakout for URLLC in industrial 5G

Industrial URLLC workloads-coordinated robotics, automated guided vehicles, machine-vision collaboration require sub-5 ms latency and five-nines reliability. In standardized 5G Multicast/Broadcast Services, intra-cell group traffic remains anchored in the core using MB-SMF/MB-UPF, and the Application Function. This incurs a core network path and packet delay that is avoidable when data transmitters and receivers share a cell. We propose a gNB-local multicast breakout that pivots eligible uplink flows to a downlink point-to-multipoint bearer within the gNB, while maintaining authorization, membership, and policy in the 5G core. The design specifies an eligibility policy, configured-grant uplink. 3GPP security and compliance are preserved via unchanged control-plane anchors. A latency budget and simulation indicate that removing the backhaul/UPF/AF segment reduces end-to-end latency from approximate 6.5-11.5 ms (anchored to the core) to 1.5-4.0 ms (local breakout), producing sub-2 ms averages and a stable gap approximate 10 ms between group sizes. The approach offers a practical, standards-aligned path to deterministic intra-cell group dissemination in private 5G. We outline multi-cell and prototype validation as future work.

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arXiv2025

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, character-level noise, and entity obfuscation for differential privacy enforcement, and combines these techniques into a unified defense mechanism. Using a binary propaganda classification dataset, baseline transformer models (BERT, GPT-2) we achieved F1 scores of 0.89 and 0.90. Applying TRIDENT's third-tier defense yields a reduced but effective cumulative F1 of 0.83, demonstrating strong privacy protection across mobile ML deployments with minimal degradation.

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arXiv2024

MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting

In this paper, we present MasonTigers' participation in SemEval-2024 Task 10, a shared task aimed at identifying emotions and understanding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for Hindi-English code-mixed dialogues, emotion flip reasoning for Hindi-English code-mixed dialogues, and emotion flip reasoning for English dialogues. Our team, MasonTigers, contributed to each subtask, focusing on developing methods for accurate emotion recognition and reasoning. By leveraging our approaches, we attained impressive F1-scores of 0.78 for the first task and 0.79 for both the second and third tasks. This performance not only underscores the effectiveness of our methods across different aspects of the task but also secured us the top rank in the first and third subtasks, and the 2nd rank in the second subtask. Through extensive experimentation and analysis, we provide insights into our system's performance and contributions to each subtask.

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arXiv2024

MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness

This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.

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arXiv2024

MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection

This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.

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arXiv2024

MasonTigers at SemEval-2024 Task 9: Solving Puzzles with an Ensemble of Chain-of-Thoughts

Our paper presents team MasonTigers submission to the SemEval-2024 Task 9 - which provides a dataset of puzzles for testing natural language understanding. We employ large language models (LLMs) to solve this task through several prompting techniques. Zero-shot and few-shot prompting generate reasonably good results when tested with proprietary LLMs, compared to the open-source models. We obtain further improved results with chain-of-thought prompting, an iterative prompting method that breaks down the reasoning process step-by-step. We obtain our best results by utilizing an ensemble of chain-of-thought prompts, placing 2nd in the word puzzle subtask and 13th in the sentence puzzle subtask. The strong performance of prompted LLMs demonstrates their capability for complex reasoning when provided with a decomposition of the thought process. Our work sheds light on how step-wise explanatory prompts can unlock more of the knowledge encoded in the parameters of large models.

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arXiv2024

MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles

The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.

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arXiv2024

MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments

In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.

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arXiv2024

MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification

The task of identifying public opinions on social media, particularly regarding climate activism and the detection of hate events, has emerged as a critical area of research in our rapidly changing world. With a growing number of people voicing either to support or oppose to climate-related issues - understanding these diverse viewpoints has become increasingly vital. Our team, MasonPerplexity, participates in a significant research initiative focused on this subject. We extensively test various models and methods, discovering that our most effective results are achieved through ensemble modeling, enhanced by data augmentation techniques like back-translation. In the specific components of this research task, our team achieved notable positions, ranking 5th, 1st, and 6th in the respective sub-tasks, thereby illustrating the effectiveness of our approach in this important field of study.

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