This article discusses an approach for both authentication of medical images and confidentiality for the related textual data in an online medical application paradigm. The image authentication is achieved in a soft manner through a feature-based digital signature while the confidentiality of the related patient information is achieved through reversible data hiding. The selected features are robust towards geometric transformations, while fragile towards texture alterations that are characteristic of medical images. The processing scheme is done in a block by block basis to permit the localization of tampered imageâ€™s regions. The effectiveness of the scheme, proven through experiments on a sample of medical images, enables us to argue that implementing mechanisms lying on this approach will help to maintain personal patient privacy and medical image integrity.

Using Statistical Texture Analysis for Medical Image Tamper Proofing

ExpSSOA-Deep maxout: Exponential Shuffled shepherd optimization based Deep maxout network for intrusion detection using big data in cloud computing framework

The existing teaching quality evaluation methods cannot calculate the distance between the data points of teaching dataset and the center points of the large density grid, which leads to the poor classification of teaching data and the low accuracy of teaching quality evaluation. Therefore, a method of teaching quality evaluation of Wushu based on fuzzy clustering is proposed. In order to improve the comprehensiveness of teaching quality evaluation, the lost data of teaching resources were recovered. Based on this, the grid index of martial arts teaching data is established, and the relationship model between teaching quality and martial arts achievement is constructed. Based on the characteristics of Wushu teaching resources, the fuzzy clustering method is introduced to calculate the distance between each data point and the grid center with high data density. The experimental results show that the evaluation accuracy of the teaching quality is high, and the lost data can be accurately recovered. The evaluation efficiency, reliability, and stability of Wushu teaching quality are ideal.

/pdf/evaluation-method-of-wushu-teaching-quality-based-on-fuzzy-2m8zkoh7.pdf

Evaluation Method of Wushu Teaching Quality Based on Fuzzy Clustering

A security model to protect the isolation of medical data in the cloud using hybrid cryptography

In order to realize the security of prison perimeter, a prison perimeter intelligent monitoring system based on cloud intelligence technology is designed. Based on the multisupply chain collaboration framework, the prison network interface submodule and sniffer monitoring structure are connected on demand to complete the hardware execution environment of the prison perimeter intelligent monitoring system. Analyze the specific service mode of the cloud smart technology host, set the necessary cloud smart technology service monitoring protocol, judge the processing information parameters, select the prison perimeter remote communication port number, set the prison perimeter network IP address and password program, complete the construction of the software execution environment of the monitoring system, and intelligently monitor and track the abnormal conditions around the prison; combined with the relevant hardware and software structure, the design of prison perimeter intelligent monitoring system based on cloud intelligence technology is realized. The experimental results show that the DPI index of the designed system has an obvious upward trend, and the processing results are close to the original image results to avoid the impact caused by the change of monitoring scene.

/pdf/intelligent-monitoring-system-for-prison-perimeter-based-on-2y1954kh.pdf

Intelligent Monitoring System for Prison Perimeter Based on Cloud Intelligence Technology

Cloud computing is currently playing an important role in the information technology industry because of its improved efficiency, wide access, low cost, and many benefits. It also provides more space for storing data and transmitting data from one location to another faster for different users on the Internet. Due to large storage, cloud customers can save huge capital investment on IT infrastructure and focus on their own core business. Therefore, many companies or organizations are moving their business to the cloud. However, many customers are reluctant to use the cloud due to security and privacy concerns. To tackle this problem, in this paper, efficient secure data retrieval is developed with the help of multi-stage authentication (MSA) and optimized blowfish algorithm (OBA). The proposed system consists of three modules namely, MSA, data security, and data retrieval. Initially, the cloud users register their information on cloud based on a multi-authentication procedure. After the registration process, the data are encrypted with the help of OBA. To increase the security of the system, the key value is optimally selected with the help of a binary crow search algorithm. After the encryption process, MSA based data retrieval process is performed. This will avoid, un-authorized person to attack the data. The performance of the proposed methodology is implemented in JAVA and performances are analyzed in terms of different metrics.

Efficient secure data retrieval on cloud using multi-stage authentication and optimized blowfish algorithm

Security﻿incidents﻿such﻿as﻿denial﻿of﻿service﻿(DoS),﻿scanning,﻿malware﻿code﻿injection,﻿viruses,﻿worms,﻿ and﻿password﻿cracking﻿are﻿becoming﻿common﻿in﻿a﻿cloud﻿environment﻿that﻿affects﻿companies﻿and﻿may﻿ produce﻿a﻿financial﻿loss﻿if﻿not﻿detected﻿in﻿time.﻿Such﻿problems﻿are﻿handled﻿by﻿presenting﻿an﻿intrusion﻿ detection﻿system﻿(IDS)﻿into﻿the﻿cloud.﻿The﻿existing﻿cloud﻿IDSs﻿affect﻿low﻿detection﻿accuracy,﻿high﻿ false﻿detection﻿rate,﻿and﻿execution﻿time.﻿To﻿overcome﻿this﻿problem,﻿in﻿this﻿article,﻿a﻿gravitational﻿ search﻿algorithm-based﻿fuzzy﻿inference﻿system﻿(GSA-FIS)﻿is﻿developed﻿as﻿intrusion﻿detection.﻿In﻿this﻿ approach,﻿fuzzy﻿parameters﻿are﻿optimized﻿using﻿GSA.﻿The﻿proposed﻿consist﻿of﻿two﻿modules﻿namely;﻿ possibilistic﻿fuzzy﻿c-means﻿(PFCM)﻿based﻿clustering,﻿training﻿based﻿on﻿the﻿GSA-FIS,﻿and﻿testing﻿ process.﻿Initially,﻿the﻿incoming﻿data﻿is﻿pre-processed﻿and﻿clustered﻿with﻿the﻿help﻿of﻿PFCM.﻿PFCM﻿ detects﻿the﻿noise﻿of﻿fuzzy﻿c-means﻿clustering﻿(FCM),﻿then﻿conquers﻿the﻿coincident﻿cluster﻿problem﻿ of﻿possibilistic﻿fuzzy﻿c-means﻿(PCM)﻿and﻿eradicate﻿the﻿row﻿sum﻿constraints﻿of﻿fuzzy﻿possibilistic﻿ c-means﻿clustering﻿(FPCM).﻿After﻿the﻿clustering﻿process,﻿the﻿clustered﻿data﻿is﻿given﻿to﻿the﻿optimized﻿ fuzzy﻿inference﻿system﻿(OFIS).﻿Here,﻿normal﻿and﻿abnormal﻿data﻿are﻿identified﻿by﻿the﻿fuzzy﻿score,﻿ while﻿the﻿training﻿is﻿done﻿by﻿the﻿GSA﻿through﻿optimizing﻿the﻿entire﻿fuzzy﻿system.﻿In﻿this﻿approach,﻿ four﻿types﻿of﻿abnormal﻿data﻿are﻿detected﻿namely-﻿probe,﻿remote﻿to﻿local﻿(R2L),﻿user﻿to﻿root﻿(U2R),﻿and﻿ DoS.﻿Simulation﻿results﻿show﻿that﻿the﻿performance﻿of﻿the﻿proposed﻿GSA-FIS﻿based﻿IDS﻿outperforms﻿ that﻿of﻿the﻿different﻿schemes﻿in﻿terms﻿of﻿precision,﻿recall﻿and﻿F-measure. KEywORDS Cloud Computing, DOS, Fuzzy Inference System, Gravitational Search Algorithm, Intrusion Detection System, Possibilistic Fuzzy C-Means, Probe, R2L

S. S. Sujatha

Papers

Efficient secure data retrieval on cloud using multi-stage authentication and optimized blowfish algorithm

An Efficient Automatic Intrusion Detection in Cloud Using Optimized Fuzzy Inference System