M. Sudha

Bio: M. Sudha is an academic researcher from VIT University. The author has contributed to research in topics: Encryption & Cryptography. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

A Privacy Preserving Hybrid Neural-Crypto Computing-Based Image Steganography for Medical Images

Abstract: With advancements in X-ray technology, there is an increase in the number of digital images used in the diagnosis of a patient. Whether it be a simple X-ray, MRI, CT scan or even a photo taken from a camera, the rise in the use of digital images has increased sharply. Though this has eased the entire process, it has brought the threat of cyber-attacks and breaches. The proposed method bridges this existing gap by incorporating suitable security mechanisms to preserve the privacy and confidentiality of medical diagnostic information of an individual. The approach utilizes neural networks to perform image steganography and combines it with a cryptographic algorithm (RSA) to secure medical images. The proposed method uses a two-level security providing a lower loss of 0.002188 on medical images improving upon existing image steganography techniques.

Security of medical images for telemedicine: a systematic review

Abstract: Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

Performance Analysis of a Bottleneck Layer Network in the Estimation of Cyber-Attacks

Abstract: Cyber-attack is an attempt made from an individual or cybercriminals to hack a particular computer or network through internet. This leads to loss of information stored in the connected system and in certain cases it leads to denial of service. The traditional methods on addressing cyber-attacks are not efficient to the complex and high sophisticated attacks. Hence the deep learning based techniques are generated in recent years for estimating the attacks presence in a communication network. However, the deep learning networks are complex in nature as they are handled with a huge range of features during its operation. Therefore a bottleneck layer was developed to reduce the parameters count and feature formulations from a given data. The residual blocks are deeper than the traditional network architectures and it is achieved by enabling a 1x1 convolution block in the design flow. The performance of the bottleneck layer on the ANN and DNN algorithm is verified in the proposed work with an openly available CIDDS-001 dataset dataset. The CIDDS dataset is one of the recent dataset consists of server traffic data on OpenStack and external severs.

A Fusional Cubic-Sine Map Model for Secure Medical Image Transmission

Abstract: Medical images are one of the moset significant attribute for diagnoising the disease in medical systems. In today modernization in digital environment medical images are hacked during transmission on insecure network. By considering the patient’s privacy and security their medical images has to be transferred in secure maner. This work aims for proposing a new medical image cipher architecture by based on fusional chaotic map. At first, a fusional Cubic-Sine Map (CSM) is proposed to generate pseudorandom numbers, then confusion and diffusion of image is executed based on the chaotic series produced by CSM. Experimental results and security analysis indicate that the developed chaotic map model generate sufficient random series and also the proposed cipher model has the ability to resisit Statistical, exhaustive, crop and noise attacks attacks.