arXiv subjects

Md. Nazmus Sakib

Publications and source records attributed to Md. Nazmus Sakib.

5 recordsLinked to original sources

arXiv2026

ShobdoSetu: A Data-Centric Framework for Bengali Long-Form Speech Recognition and Speaker Diarization

Bengali is spoken by over 230 million people yet remains severely under-served in automatic speech recognition (ASR) and speaker diarization research. In this paper, we present our system for the DL Sprint 4.0 Bengali Long-Form Speech Recognition (Task~1) and Bengali Speaker Diarization Challenge (Task~2). For Task~1, we propose a data-centric pipeline that constructs a high-quality training corpus from Bengali YouTube audiobooks and dramas \cite{tabib2026bengaliloop}, incorporating LLM-assisted language normalization, fuzzy-matching-based chunk boundary validation, and muffled-zone augmentation. Fine-tuning the \texttt{tugstugi/whisper-medium} model on approximately 21,000 data points with beam size 5, we achieve a Word Error Rate (WER) of 16.751 on the public leaderboard and 15.551 on the private test set. For Task~2, we fine-tune the pyannote.audio community-1 segmentation model with targeted hyperparameter optimization under an extreme low-resource setting (10 training files), achieving a Diarization Error Rate (DER) of 0.19974 on the public leaderboard, and .26723 on the private test set. Our results demonstrate that careful data engineering and domain-adaptive fine-tuning can yield competitive performance for Bengali speech processing even without large annotated corpora.

cs.CL↗

arXiv2025

Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition

Bengali, spoken by over 300 million people, is a morphologically rich and lowresource language, posing challenges for automatic speech recognition (ASR). This research presents an end-to-end framework for Bengali ASR, building on a Conformer-CTC backbone with a multi-level embedding fusion mechanism that incorporates phoneme, syllable, and wordpiece representations. By enriching acoustic features with these linguistic embeddings, the model captures fine-grained phonetic cues and higher-level contextual patterns. The architecture employs early and late Conformer stages, with preprocessing steps including silence trimming, resampling, Log-Mel spectrogram extraction, and SpecAugment augmentation. The experimental results demonstrate the strong potential of the model, achieving a word error rate (WER) of 10.01% and a character error rate (CER) of 5.03%. These results demonstrate the effectiveness of combining multi-granular linguistic information with acoustic modeling, providing a scalable approach for low-resource ASR development.

eess.AS↗

arXiv2025

Enhancing Network Security: A Hybrid Approach for Detection and Mitigation of Distributed Denial-of-Service Attacks Using Machine Learning

The distributed denial-of-service (DDoS) attack stands out as a highly formidable cyber threat, representing an advanced form of the denial-of-service (DoS) attack. A DDoS attack involves multiple computers working together to overwhelm a system, making it unavailable. On the other hand, a DoS attack is a one-on-one attempt to make a system or website inaccessible. Thus, it is crucial to construct an effective model for identifying various DDoS incidents. Although extensive research has focused on binary detection models for DDoS identification, they face challenges to adapt evolving threats, necessitating frequent updates. Whereas multiclass detection models offer a comprehensive defense against diverse DDoS attacks, ensuring adaptability in the ever-changing cyber threat landscape. In this paper, we propose a Hybrid Model to strengthen network security by combining the featureextraction abilities of 1D Convolutional Neural Networks (CNNs) with the classification skills of Random Forest (RF) and Multi-layer Perceptron (MLP) classifiers. Using the CIC-DDoS2019 dataset, we perform multiclass classification of various DDoS attacks and conduct a comparative analysis of evaluation metrics for RF, MLP, and our proposed Hybrid Model. After analyzing the results, we draw meaningful conclusions and confirm the superiority of our Hybrid Model by performing thorough cross-validation. Additionally, we integrate our machine learning model with Snort, which provides a robust and adaptive solution for detecting and mitigating various DDoS attacks.

cs.CR↗

arXiv2024

A Comparative Study of Watering Hole Attack Detection Using Supervised Neural Network

The state of security demands innovative solutions to defend against targeted attacks due to the growing sophistication of cyber threats. This study explores the nefarious tactic known as "watering hole attacks using supervised neural networks to detect and prevent these attacks. The neural network identifies patterns in website behavior and network traffic associated with such attacks. Testing on a dataset of confirmed attacks shows a 99% detection rate with a mere 0.1% false positive rate, demonstrating the model's effectiveness. In terms of prevention, the model successfully stops 95% of attacks, providing robust user protection. The study also suggests mitigation strategies, including web filtering solutions, user education, and security controls. Overall, this research presents a promising solution for countering watering hole attacks, offering strong detection, prevention, and mitigation strategies.

cs.CR↗

arXiv2023

Decentralised Identity Federations using Blockchain

Federated Identity Management has proven its worth by offering economic benefits and convenience to Service Providers and users alike. In such federations, the Identity Provider (IdP) is the solitary entity responsible for managing user credentials and generating assertions for the users, who are requesting access to a service provider's resource. This makes the IdP centralised and exhibits a single point of failure for the federation, making the federation prone to catastrophic damages. The paper presents our effort in designing and implementing a decentralised system in establishing an identity federation. In its attempt to decentralise the IdP in the federation, the proposed system relies on blockchain technology, thereby mitigating the single point of failure shortcoming of existing identity federations. The system is designed using a set of requirements In this article, we explore different aspects of designing and developing the system, present its protocol flow, analyse its performance, and evaluate its security using ProVerif, a state-of-the-art formal protocol verification tool.

cs.CR↗