Md Morshedul Islam

Asif Alif, Khondokar Fida Hasan, Basker Palaniswamy et al.

Smart healthcare industry is increasingly relying on Internet of Things (IoT) devices to improve patient care and operational efficiency. However, the cryptographic algorithms that enable fundamental security and are widely used in these cyber systems are vulnerable to attacks by emerging quantum computers - known as Quantum Threat. This paper examines the quantum threat to healthcare IoT across the four layers of the IoT architecture: physical, network, perception, and application. It proposes a comprehensive migration framework integrating a phased hybrid approach with crypto-agility to transition healthcare IoT systems to quantum-safe cryptography. This framework prioritises resource-constrained devices, emphasises interoperability, and considers the challenges of vendor readiness and infrastructure upgrades. This paper contributes a detailed, phased migration plan specifically tailored to the unique security needs and resource limitations of IoT-based healthcare systems.

Md Morshedul Islam, Md Abdur Rafiq

A behavioral authentication (BA) system uses the behavioral characteristics of users to verify their identity claims. A BA verification algorithm can be constructed by training a neural network (NN) classifier on users' profiles. The trained NN model classifies the presented verification data, and if the classification matches the claimed identity, the verification algorithm accepts the claim. This classification-based approach removes the need to maintain a profile database. However, similar to other NN architectures, the NN classifier of the BA system is vulnerable to privacy attacks. To protect the privacy of training and test data used in an NN different techniques are widely used. In this paper, our focus is on a non-crypto-based approach, and we used random projection (RP) to ensure data privacy in an NN model. RP is a distance-preserving transformation based on a random matrix. Before sharing the profiles with the verifier, users will transform their profiles by RP and keep their matrices secret. To reduce the computation load in RP, we use sparse random projection, which is very effective for low-compute devices. Along with correctness and security properties, our system can ensure the changeability property of the BA system. We also introduce an ML-based privacy attack, and our proposed system is robust against this and other privacy and security attacks. We implemented our approach on three existing behavioral BA systems and achieved a below 2.0% FRR and a below 1.0% FAR rate. Moreover, the machine learning-based privacy attacker can only recover below 3.0% to 12.0% of features from a portion of the projected profiles. However, these recovered features are not sufficient to know details about the users' behavioral pattern or to be used in a subsequent attack. Our approach is general and can be used in other NN-based BA systems as well as in traditional biometric systems.

Wali Mohammad Abdullah, Md. Morshedul Islam, Devraj Parmar et al.

Large Language Models (LLMs) like GPT-3.5-Turbo are increasingly used to assist software development, yet they often produce incomplete code or incorrect imports, especially when lacking access to external or project-specific documentation. We introduce RAILS (Retrieval-Augmented Intelligence for Learning Software Development), a framework that augments LLM prompts with semantically retrieved context from curated Java resources using FAISS and OpenAI embeddings. RAILS incorporates an iterative validation loop guided by compiler feedback to refine suggestions. We evaluated RAILS on 78 real-world Java import error cases spanning standard libraries, GUI APIs, external tools, and custom utilities. Despite using the same LLM, RAILS outperforms baseline prompting by preserving intent, avoiding hallucinations, and surfacing correct imports even when libraries are unavailable locally. Future work will integrate symbolic filtering via PostgreSQL and extend support to other languages and IDEs.