arXiv subjects

Hasibul Hossain Shajeeb

Publications and source records attributed to Hasibul Hossain Shajeeb.

3 recordsLinked to original sources

arXiv2025

ISADM: An Integrated STRIDE, ATT&CK, and D3FEND Model for Threat Modeling Against Real-world Adversaries

FinTechs increasing connectivity, rapid innovation, and reliance on global digital infrastructures present significant cybersecurity challenges. Traditional cybersecurity frameworks often struggle to identify and prioritize sector-specific vulnerabilities or adapt to evolving adversary tactics, particularly in highly targeted sectors such as FinTech. To address these gaps, we propose ISADM (Integrated STRIDE-ATTACK-D3FEND Threat Model), a novel hybrid methodology applied to FinTech security that integrates STRIDE's asset-centric threat classification with MITRE ATTACK's catalog of real-world adversary behaviors and D3FEND's structured knowledge of countermeasures. ISADM employs a frequency-based scoring mechanism to quantify the prevalence of adversarial Tactics, Techniques, and Procedures (TTPs), enabling a proactive, score-driven risk assessment and prioritization framework. This proactive approach contributes to shifting organizations from reactive defense strategies toward the strategic fortification of critical assets. We validate ISADM through industry-relevant case study analyses, demonstrating how the approach replicates actual attack patterns and strengthens proactive threat modeling, guiding risk prioritization and resource allocation to the most critical vulnerabilities. Overall, ISADM offers a comprehensive hybrid threat modeling methodology that bridges asset-centric and adversary-centric analysis, providing FinTech systems with stronger defenses. The emphasis on real-world validation highlights its practical significance in enhancing the sector's cybersecurity posture through a frequency-informed, impact-aware prioritization scheme that combines empirical attacker data with contextual risk analysis.

cs.CR↗

arXiv2025

Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection

This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and analysed 11 deep learning models using COVID-19 image, text, and speech (ie, cough) data. Our analysis revealed that the MobileNet model achieved the highest accuracy of 99.97% for COVID-19 image data and 93.73% for speech data (i.e., cough). However, the BiGRU model demonstrated superior performance in COVID-19 text classification with an accuracy of 99.89%. The broader implications of this research suggest potential benefits for other domains and disciplines that could leverage deep learning techniques for image, text, and speech analysis.

cs.LG↗

arXiv2023

Blockchain-Enabled Federated Learning Approach for Vehicular Networks

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system's accuracy stands at 91.92\%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.

cs.LG↗