arXiv subjects

Munshi Saifuzzaman

Publications and source records attributed to Munshi Saifuzzaman.

5 recordsLinked to original sources

arXiv2026

CellSecInspector: Safeguarding Cellular Networks via Automated Security Analysis on Specifications

The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requirements, fail to capture deep semantic dependencies, cross-sentence/clause relationships, and evolving specification behaviors. In this work, we present CellSecInspector, an automated framework for security analysis of 3GPP specifications. CellSecInspector extracts structured state-condition-action (SCA) representations, models mobile network procedures with comprehensive function chains, systematically validates them against 9 foundational security properties under 4 adversarial scenarios, and automatically generates test cases. This end-to-end approach enables the automated discovery of vulnerabilities without relying on manually predefined security requirements or rules. Applying CellSecInspector to the well-studied 5G and 4G NAS and RRC specifications and selected sections of TS 23.501 and TS 24.229, it discovers 43 vulnerabilities, 7 of which are previously unreported. Our findings show that CellSecInspector is a scalable, adaptive, and effective solution to assess 3GPP specifications for safeguarding operational and next-generation cellular networks.

cs.CR↗

arXiv2024

Introduction to IoT

The Internet of Things has rapidly transformed the 21st century, enhancing decision-making processes and introducing innovative consumer services such as pay-as-you-use models. The integration of smart devices and automation technologies has revolutionized every aspect of our lives, from health services to the manufacturing industry, and from the agriculture sector to mining. Alongside the positive aspects, it is also essential to recognize the significant safety, security, and trust concerns in this technological landscape. This chapter serves as a comprehensive guide for newcomers interested in the IoT domain, providing a foundation for making future contributions. Specifically, it discusses the overview, historical evolution, key characteristics, advantages, architectures, taxonomy of technologies, and existing applications in major IoT domains. In addressing prevalent issues and challenges in designing and deploying IoT applications, the chapter examines security threats across architectural layers, ethical considerations, user privacy concerns, and trust-related issues. This discussion equips researchers with a solid understanding of diverse IoT aspects, providing a comprehensive understanding of IoT technology along with insights into the extensive potential and impact of this transformative field.

cs.CR↗

arXiv2024

Towards Smart Healthcare: Challenges and Opportunities in IoT and ML

The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. The traditional healthcare system, centered around hospitals and clinics, has proven inadequate in the face of such challenges. Intelligent wearable devices, a key part of modern healthcare, leverage Internet of Things technology to collect extensive data related to the environment as well as psychological, behavioral, and physical health. However, managing the substantial data generated by these wearables and other IoT devices in healthcare poses a significant challenge, potentially impeding decision-making processes. Recent interest has grown in applying data analytics for extracting information, gaining insights, and making predictions. Additionally, machine learning, known for addressing various big data and networking challenges, has seen increased implementation to enhance IoT systems in healthcare. This chapter focuses exclusively on exploring the hurdles encountered when integrating ML methods into the IoT healthcare sector. It offers a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios: IoT-based, ML-based, and the implementation of machine learning methodologies in the IoT-based healthcare industry. This compilation will assist future researchers, healthcare professionals, and government agencies by offering valuable insights into recent smart healthcare advancements.

cs.CY↗

arXiv2023

Managing health insurance using blockchain technology

Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance. However, from granting health insurance to delivering services to insured individuals, the health insurance industry faces numerous obstacles. Fraudulent actions, false claims, a lack of transparency and data privacy, reliance on human effort and dishonesty from consumers, healthcare professionals, or even the insurer party itself, are the most common and important hurdles towards success. Given these constraints, this chapter briefly covers the most immediate concerns in the health insurance industry and provides insight into how blockchain technology integration can contribute to resolving these issues. This chapter finishes by highlighting existing limitations as well as potential future directions.

cs.CY↗

arXiv2021

A Systematic Literature Review on Wearable Health Data Publishing under Differential Privacy

Wearable devices generate different types of physiological data about the individuals. These data can provide valuable insights for medical researchers and clinicians that cannot be availed through traditional measures. Researchers have historically relied on survey responses or observed behavior. Interestingly, physiological data can provide a richer amount of user cognition than that obtained from any other sources, including the user himself. Therefore, the inexpensive consumer-grade wearable devices have become a point of interest for the health researchers. In addition, they are also used in continuous remote health monitoring and sometimes by the insurance companies. However, the biggest concern for such kind of use cases is the privacy of the individuals. There are a few privacy mechanisms, such as abstraction and k-anonymity, are widely used in information systems. Recently, Differential Privacy (DP) has emerged as a proficient technique to publish privacy sensitive data, including data from wearable devices. In this paper, we have conducted a Systematic Literature Review (SLR) to identify, select and critically appraise researches in DP as well as to understand different techniques and exiting use of DP in wearable data publishing. Based on our study we have identified the limitations of proposed solutions and provided future directions.

cs.CR↗