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Upama Kabir

Publications and source records attributed to Upama Kabir.

3 recordsLinked to original sources

An End-to-End Authentication Mechanism for Wireless Body Area Networks

Wireless Body Area Network (WBAN) ensures high-quality healthcare services by endowing distant and continual monitoring of patients' health conditions. The security and privacy of the sensitive health-related data transmitted through the WBAN should be preserved to maximize its benefits. In this regard, user authentication is one of the primary mechanisms to protect health data that verifies the identities of entities involved in the communication process. Since WBAN carries crucial health data, every entity engaged in the data transfer process must be authenticated. In literature, an end-to-end user authentication mechanism covering each communicating party is absent. Besides, most of the existing user authentication mechanisms are designed assuming that the patient's mobile phone is trusted. In reality, a patient's mobile phone can be stolen or comprised by malware and thus behaves maliciously. Our work addresses these drawbacks and proposes an end-to-end user authentication and session key agreement scheme between sensor nodes and medical experts in a scenario where the patient's mobile phone is semi-trusted. We present a formal security analysis using BAN logic. Besides, we also provide an informal security analysis of the proposed scheme. Both studies indicate that our method is robust against well-known security attacks. In addition, our scheme achieves comparable computation and communication costs concerning the related existing works. The simulation shows that our method preserves satisfactory network performance.

cs.CR

Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification

Magnetic Resonance Imaging (MRI) is a principal diagnostic approach used in the field of radiology to create images of the anatomical and physiological structure of patients. MRI is the prevalent medical imaging practice to find abnormalities in soft tissues. Traditionally they are analyzed by a radiologist to detect abnormalities in soft tissues, especially the brain. The process of interpreting a massive volume of patient's MRI is laborious. Hence, the use of Machine Learning methodologies can aid in detecting abnormalities in soft tissues with considerable accuracy. In this research, we have curated a novel dataset and developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images. In this paper, we adopted the Deep Residual Convolutional Neural Network (ResNet50) architecture for the experiments along with discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40% on the curated dataset, 93.80% on the Harvard Whole Brain Atlas dataset, and 97.05% accuracy on the School of Biomedical Engineering dataset. Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.

cs.CV

A New Routing Protocol for All Optical Network

In this research paper, an efficient routing protocol for all optical network (AOL) is proposed. The technique uses wavelength division multiplexing (WDM). The proposed one is different from the conventional AOL protocol in transmission of data and control over optical fiber. A set of wavelengths is reserved to transfer control information, which is defined as control wavelengths. Control wavelengths are routed with packet routing scheme and the others are routed with wavelength routing scheme. In connection oriented network only the control packets are sent with the control wavelengths, data or messages are sent with other wavelengths. On the other hand, in datagram network, all the packets are sent with the control wavelengths. The allocation of wavelengths may be fully dynamic.

cs.NI