arXiv subjects

Nazmul Islam

Publications and source records attributed to Nazmul Islam.

9 recordsLinked to original sources

arXiv2026

Lifting the degeneracy of quantum spin liquid phase by uniaxial pressure

We report muon spin relaxation/rotation ($\mu$SR) measurements of the candidate three-dimensional (3D) quantum spin liquid (QSL) PbCuTe$_2$O$_6$, hosting $S=1/2$ moments, under controlled in situ [110] uniaxial compression up to $\sigma_{[110]}=37.7$~MPa. A small directional lattice perturbation significantly modifies the local magnetic response, while above $\sigma_{\rm cr}\sim10.8$\,MPa the relaxation rates are strongly enhanced and the internal-field distribution is substantially broadened. These changes occur along with the local crystalline symmetry breaking. While, no evidence for conventional static long-range magnetic order is observed, the compression drives the system towards a structurally modified and strongly correlated state in which enhanced quasi-static correlations coexist with persistent slow spin dynamics. This work demonstrates a clean and symmetry-selective route to control frustrated exchange landscape and access hidden magnetic instabilities in a 3D QSL candidate opening up the possibilities to tune other correlated systems where intrinsic coupling between magnetic and lattice degrees of freedom are relevant.

cond-mat.str-el↗

arXiv2026

CONTEX-T: Contextual Exploitation of Encrypted Traffic for Device Fingerprinting via Transformer Time-Frequency Analysis

The rapid expansion of internet of things (IoT) devices has created a pervasive ecosystem where encrypted wireless communications serve as the primary privacy and security protection mechanism. While encryption effectively protects message content, contextual information from packet metadata and statistics inadvertently expose device identities. Various studies have exploited raw packet statistics and their visual representations for device fingerprinting and identification. However, these approaches remain confined to the spatial domain with limited feature representation. Therefore, this paper presents CONTEX-T, a novel framework that exploits device-level information from encrypted traffic metadata using temporal and spectral representation. The experiments show that time-frequency analysis provides new and rich feature representation, revealing a complex and expanding threat landscape that would require robust countermeasures for IoT security management. CONTEX-T first transforms raw packet-length sequences into temporal and spectral representations and then utilizes vision transformers (ViTs) for device identification. We systematically evaluated multiple time-frequency representation techniques and transformer-based models across encrypted traffic samples from various IoT devices. CONTEX-T achieved device classification accuracy exceeding 99% while operating passively on observable contextual metadata. This demonstrates that temporal and spectral signatures persist under strong encryption, highlighting a critical attack surface for IoT network security and management.

cs.CR↗

arXiv2025

ShrimpXNet: A Transfer Learning Framework for Shrimp Disease Classification with Augmented Regularization, Adversarial Training, and Explainable AI

Shrimp is one of the most widely consumed aquatic species globally, valued for both its nutritional content and economic importance. Shrimp farming represents a significant source of income in many regions; however, like other forms of aquaculture, it is severely impacted by disease outbreaks. These diseases pose a major challenge to sustainable shrimp production. To address this issue, automated disease classification methods can offer timely and accurate detection. This research proposes a deep learning-based approach for the automated classification of shrimp diseases. A dataset comprising 1,149 images across four disease classes was utilized. Six pretrained deep learning models, ResNet50, EfficientNet, DenseNet201, MobileNet, ConvNeXt-Tiny, and Xception were deployed and evaluated for performance. The images background was removed, followed by standardized preprocessing through the Keras image pipeline. Fast Gradient Sign Method (FGSM) was used for enhancing the model robustness through adversarial training. While advanced augmentation strategies, including CutMix and MixUp, were implemented to mitigate overfitting and improve generalization. To support interpretability, and to visualize regions of model attention, post-hoc explanation methods such as Grad-CAM, Grad-CAM++, and XGrad-CAM were applied. Exploratory results demonstrated that ConvNeXt-Tiny achieved the highest performance, attaining a 96.88% accuracy on the test dataset. After 1000 iterations, the 99% confidence interval for the model is [0.953,0.971].

cs.LG↗

arXiv2025

Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction

Machine learning (ML) in Internet of Vehicles (IoV) applications enhanced intelligent transportation, autonomous driving capabilities, and various connected services within a large, heterogeneous network. However, the increased connectivity and massive data exchange for ML applications introduce significant privacy challenges. Privacy-preserving machine learning (PPML) offers potential solutions to address these challenges by preserving privacy at various stages of the ML pipeline. Despite the rapid development of ML-based IoV applications and the growing data privacy concerns, there are limited comprehensive studies on the adoption of PPML within this domain. Therefore, this study provides a comprehensive review of the fundamentals, recent advancements, and the challenges of integrating PPML into IoV applications. We first review existing surveys of various PPML techniques and their integration into IoV across different scopes. We then categorize IoV applications into three key domains and analyze the privacy challenges in leveraging ML in these application domains. Building on these fundamentals, we review recent advancements in integrating various PPML techniques within IoV applications, discussing their frameworks, key features, and performance in terms of privacy, utility, and efficiency. Finally, we identify current challenges and propose future research directions to enhance privacy and reliability in IoV applications.

cs.CR↗

arXiv2024

Cyber Threats to Canadian Federal Election: Emerging Threats, Assessment, and Mitigation Strategies

As Canada prepares for the 2025 federal election, ensuring the integrity and security of the electoral process against cyber threats is crucial. Recent foreign interference in elections globally highlight the increasing sophistication of adversaries in exploiting technical and human vulnerabilities. Such vulnerabilities also exist in Canada's electoral system that relies on a complex network of IT systems, vendors, and personnel. To mitigate these vulnerabilities, a threat assessment is crucial to identify emerging threats, develop incident response capabilities, and build public trust and resilience against cyber threats. Therefore, this paper presents a comprehensive national cyber threat assessment, following the NIST Special Publication 800-30 framework, focusing on identifying and mitigating cybersecurity risks to the upcoming 2025 Canadian federal election. The research identifies three major threats: misinformation, disinformation, and malinformation (MDM) campaigns; attacks on critical infrastructure and election support systems; and espionage by malicious actors. Through detailed analysis, the assessment offers insights into the capabilities, intent, and potential impact of these threats. The paper also discusses emerging technologies and their influence on election security and proposes a multi-faceted approach to risk mitigation ahead of the election.

cs.CR↗

arXiv2024

Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications

Deep learning (DL) has revolutionized wireless communication systems by introducing datadriven end-to-end (E2E) learning, where the physical layer (PHY) is transformed into DL architectures to achieve peak optimization. Leveraging DL for E2E optimization in PHY significantly enhances its adaptability and performance in complex wireless environments, meeting the demands of advanced network systems such as 5G and beyond. Furthermore, this evolution of data-driven PHY optimization has also enabled advanced semantic applications across various modalities, including text, image, audio, video, and multimodal transmissions. These applications elevate communication from bit-level to semantic-level intelligence, making it capable of discerning context and intent. Although the PHY, as a DL architecture, plays a crucial role in enabling semantic communication (SemCom) systems, comprehensive studies that integrate both E2E communication and SemCom systems remain significantly underexplored. This highlights the novelty and potential of these integrative fields, marking them as a promising research domain. Therefore, this article provides a comprehensive review of the emerging field of data-driven PHY for E2E communication systems, emphasizing their role in enabling semantic applications across various modalities. It also identifies key challenges and potential research directions, serving as a crucial guide for future advancements in DL for E2E communication and SemCom systems.

cs.NI↗

arXiv2023

Review of Deep Learning-based Malware Detection for Android and Windows System

Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (AI) enable and can deceive traditional anti-malware systems using different obfuscation techniques. Therefore, only AI-enabled anti-malware system is robust against these techniques and can detect different features in the malware files that aid in malicious activities. In this study we review two AI-enabled techniques for detecting malware in Windows and Android operating system, respectively. Both the techniques achieved perfect accuracy in detecting various malware families.

cs.LG↗

arXiv2021

Neutron diffraction of field-induced magnon condensation in the spin-dimerized antiferromagnet Sr$_{3}$Cr$_{2}$O$_{8}$

In this work, we investigate the evolution and settling of magnon condensation in the spin-1/2 dimer system Sr$_{3}$Cr$_{2}$O$_{8}$ using a combination of magnetostriction in pulsed fields and inelastic neutron scattering in a continuous magnetic field. The magnetic structure in the Bose-Einstein condensation (BEC) phase was probed by neutron diffraction in pulsed magnetic fields up to 39~T. The magnetic structure in this phase was confirmed to be an XY-antiferromagnetic structure validated by irreducible representational analysis. The magnetic phase diagram as a function of an applied magnetic field for this system is presented. Furthermore, zero-field neutron diffraction results indicate that dimerization plays an important role in stabilizing the low-temperature crystal structure.

cond-mat.str-el↗

arXiv2015

An Incoercible E-Voting Scheme Based on Revised Simplified Verifiable Re-encryption Mix-nets

Simplified verifiable re-encryption mix-net (SVRM) is revised and a scheme for e-voting systems is developed based on it. The developed scheme enables e-voting systems to satisfy all essential requirements of elections. Namely, they satisfy requirements about privacy, verifiability, fairness and robustness. It also successfully protects voters from coercers except cases where the coercers force voters to abstain from elections. In detail, voters can conceal correspondences between them and their votes, anyone can verify the accuracy of election results, and interim election results are concealed from any entity. About incoercibility, provided that erasable-state voting booths which disable voters to memorize complete information exchanged between them and election authorities for constructing votes are available, coercer C cannot know candidates that voters coerced by C had chosen even if the candidates are unique to the voters. In addition, elections can be completed without reelections even when votes were handled illegitimately.

cs.CR↗