Moinul Hossain

Moinul Hossain, Sourav Rabi Das, Zikrul Shariar Ayon et al.

Legal practitioners and judicial institutions face an ever-growing volume of case-law documents characterised by formalised language, lengthy sentence structures, and highly specialised terminology, making manual triage both time-consuming and error-prone. This work presents a lightweight yet high-accuracy framework for citation-treatment classification that pairs lemmatisation-based preprocessing with subword-aware FastText embeddings and a multi-kernel one-dimensional Convolutional Neural Network (CNN). Evaluated on a publicly available corpus of 25,000 annotated legal documents with a 75/25 training-test partition, the proposed system achieves 97.26% classification accuracy and a macro F1-score of 96.82%, surpassing established baselines including fine-tuned BERT, Long Short-Term Memory (LSTM) with FastText, CNN with random embeddings, and a Term Frequency-Inverse Document Frequency (TF-IDF) k-Nearest Neighbour (KNN) classifier. The model also attains the highest Area Under the Receiver Operating Characteristic (AUC-ROC) curve of 97.83% among all compared systems while operating with only 5.1 million parameters and an inference latency of 0.31 ms per document - more than 13 times faster than BERT. Ablation experiments confirm the individual contribution of each pipeline component, and the confusion matrix reveals that residual errors are confined to semantically adjacent citation categories. These findings indicate that carefully designed convolutional architectures represent a scalable, resource-efficient alternative to heavyweight transformers for intelligent legal document analysis.

Md Nahid Hasan Shuvo, Moinul Hossain, Anik Mallik et al.

Federated Learning (FL) enables collaborative model training across distributed devices while safeguarding data and user privacy. However, FL remains susceptible to privacy threats that can compromise data via direct means. That said, indirectly compromising the confidentiality of the FL model architecture (e.g., a convolutional neural network (CNN) or a recurrent neural network (RNN)) on a client device by an outsider remains unexplored. If leaked, this information can enable next-level attacks tailored to the architecture. This paper proposes a novel side-channel fingerprinting attack, leveraging flow-level and packet-level statistics of encrypted wireless traffic from an FL client to infer its deep learning model architecture. We name it FLARE, a fingerprinting framework based on FL Architecture REconnaissance. Evaluation across various CNN and RNN variants-including pre-trained and custom models trained over IEEE 802.11 Wi-Fi-shows that FLARE achieves over 98% F1-score in closed-world and up to 91% in open-world scenarios. These results reveal that CNN and RNN models leak distinguishable traffic patterns, enabling architecture fingerprinting even under realistic FL settings with hardware, software, and data heterogeneity. To our knowledge, this is the first work to fingerprint FL model architectures by sniffing encrypted wireless traffic, exposing a critical side-channel vulnerability in current FL systems.

Md Nahid Hasan Shuvo, Moinul Hossain

Connected autonomous vehicles (CAVs) rely on vision-based deep neural networks (DNNs) and low-latency (Vehicle-to-Everything) V2X communication to navigate safely and efficiently. Despite their advances, these systems remain vulnerable to physical adversarial attacks. In this paper, we introduce PHANTOM (PHysical ANamorphic Threats Obstructing connected vehicle Mobility), a novel framework for crafting and deploying perspective-dependent adversarial examples using \textit{anamorphic art}. PHANTOM exploits geometric distortions that appear natural to humans but are misclassified with high confidence by state-of-the-art object detectors. Unlike conventional attacks, PHANTOM operates in black-box settings without model access and demonstrates strong transferability across four diverse detector architectures (YOLOv5, SSD, Faster R-CNN, and RetinaNet). Comprehensive evaluation in CARLA across varying speeds, weather conditions, and lighting scenarios shows that PHANTOM achieves over 90\% attack success rate under optimal conditions and maintains 60-80\% effectiveness even in degraded environments. The attack activates within 6-10 meters of the target, providing insufficient time for safe maneuvering. Beyond individual vehicle deception, PHANTOM triggers network-wide disruption in CAV systems: SUMO-OMNeT++ co-simulation demonstrates that false emergency messages propagate through V2X links, increasing Peak Age of Information by 68-89\% and degrading safety-critical communication. These findings expose critical vulnerabilities in both perception and communication layers of CAV ecosystems.

Md Nahid Hasan Shuvo, Moinul Hossain

Federated Learning (FL) is increasingly adopted as a decentralized machine learning paradigm due to its capability to preserve data privacy by training models without centralizing user data. However, FL is susceptible to indirect privacy breaches via network traffic analysis-an area not explored in existing research. The primary objective of this research is to study the feasibility of fingerprinting deep learning models deployed within FL environments by analyzing their network-layer traffic information. In this paper, we conduct an experimental evaluation using various deep learning architectures (i.e., CNN, RNN) within a federated learning testbed. We utilize machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Gradient-Boosting, to fingerprint unique patterns within the traffic data. Our experiments show high fingerprinting accuracy, achieving 100% accuracy using Random Forest and around 95.7% accuracy using SVM and Gradient Boosting classifiers. This analysis suggests that we can identify specific architectures running within the subsection of the network traffic. Hence, if an adversary knows about the underlying DL architecture, they can exploit that information and conduct targeted attacks. These findings suggest a notable security vulnerability in FL systems and the necessity of strengthening it at the network level.

Md Rashedur Rahman, Elizabeth Miller, Moinul Hossain et al.

As data privacy continues to be a crucial human-right concern as recognized by the UN, regulatory agencies have demanded developers obtain user permission before accessing user-sensitive data. Mainly through the use of privacy policies statements, developers fulfill their legal requirements to keep users abreast of the requests for their data. In addition, platforms such as Android enforces explicit permission request using the permission model. Nonetheless, recent research has shown that service providers hardly make full disclosure when requesting data in these statements. Neither is the current permission model designed to provide adequate informed consent. Often users have no clear understanding of the reason and scope of usage of the data request. This paper proposes an unambiguous, informed consent process that provides developers with a standardized method for declaring Intent. Our proposed Intent-aware permission architecture extends the current Android permission model with a precise mechanism for full disclosure of purpose and scope limitation. The design of which is based on an ontology study of data requests purposes. The overarching objective of this model is to ensure end-users are adequately informed before making decisions on their data. Additionally, this model has the potential to improve trust between end-users and developers.