Supriya Chakraborty

Supriya Chakraborty, Tamoghno Das, Manan Suri

This study presents a methodology for anticounterfeiting of Non-Volatile Memory (NVM) chips. In particular, we experimentally demonstrate a generalized methodology for detecting (i) Integrated Circuit (IC) origin, (ii) recycled or used NVM chips, and (iii) identification of used locations (addresses) in the chip. Our proposed methodology inspects latency and variability signatures of Commercial-Off-The-Shelf (COTS) NVM chips. The proposed technique requires low-cycle (~100) pre-conditioning and utilizes Machine Learning (ML) algorithms. We observe different trends in evolution of latency (sector erase or page write) with cycling on different NVM technologies from different vendors. ML assisted approach is utilized for detecting IC manufacturers with 95.1 % accuracy obtained on prepared test dataset consisting of 3 different NVM technologies including 6 different manufacturers (9 types of chips).

Dibyendu Ghosh, Srija Chakraborty, Hariprasad Kodamana et al.

Inclusion of high throughput technologies in the field of biology has generated massive amounts of biological data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the task. Hence, researchers are turning to machine learning based approaches for the analysis of high-dimensional big data. In machine learning, once a model is trained with a training dataset, it can be applied on a testing dataset which is independent. In current times, deep learning algorithms further promote the application of machine learning in several field of biology including plant virology. Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning and comprehensively discusses the trends and prospects of machine learning in diagnosis of viral diseases, understanding host-virus interplay and emergence of plant viruses.

Abhilash Garg, Supriya Chakraborty, Manoj Malik et al.

Semiconductor NAND Flash based memory technology dominates the electronic Non-Volatile storage media market. Though NAND Flash offers superior performance and reliability over conventional magnetic HDDs, yet it suffers from certain data-security vulnerabilities. Such vulnerabilities can expose sensitive information stored on the media to security risks. It is thus necessary to study in detail the fundamental reasons behind data-security vulnerabilities of NAND Flash for use in critical applications. In this paper, the problem of unreliable data-deletion/sanitization in commercial NAND Flash media is investigated along with the fundamental reasons leading to such vulnerabilities. Exhaustive software based data recovery experiments (multiple iterations) has been carried out on commercial NAND Flash storage media (8 GB and 16 GB) for different types of filesystems (NTFS and FAT) and OS specific delete/Erase instructions. 100 % data recovery is obtained for windows and linux based delete/Erase commands. Inverse effect of performance enhancement techniques like wear levelling, bad block management etc. is also observed with the help of software based recovery experiments.