RAI-DD: Reliability, Availability Identification & Dynamic Decision Based Replica Distribution for Cloud Computing

Abstract: Cloud computing is the technology based extension for distributed and grid computing. Its main aim is to share the resources in an effective and efficient manner. Cloud technology follows various characteristics for such improved sharing like utility and autonomic computing. Autonomic computing means fault tolerant and self-recoverable system. Its services are built something like virtualization of computing power economically available to large number of users. As the scalability and autonomic computing increases with more shared resources with higher degree of communication, the node failures compare to conventional systems is increased. Thus, new tools and approaches are needed to build reliable and robust systems. In this work a new RAI-DD (Reliability, Availability Identification & Dynamic Decision) based replica distribution architecture is proposed to better utilize associated scalability of computing cloud and to provide client transparent novel fault tolerant system for various cloud applications. At the initial level of work the approach is proving its efficiency by high scalability and dynamic load balancing. At the time of uneven and dropdown time the system sustains the fault tolerance and in all condition low overhead is desired. 

Index Terms: Cloud Computing, Fault Tolerance, Replica Distribution, Metrics Oriented, Availability, RAI-DD (Reliability, Availability Identification & Dynamic Decision)

Publication Details - 

Title	RAI-DD: Reliability, Availability Identification & Dynamic Decision Based Replica Distribution for Cloud Computing
Co-Author	Gaurav Shrivastava**
Publications	International Journal of Computer Science and Information Technologies,  Vol. 5 (4) , 2014,
Date & Year	2014
Volume	Vol-5 Issue-4, 2014 Page No. 5497-5502
ISSN No.	ISSN 0975-9646

Download Full Length Paper

A Hybrid Model for Intrusion Detection Based on Genetic Clustering and PSO Algorithm

ABSTRACT:-  Reduction and selection of intruder attribute in intrusion detection system play an important role in process of detection. The huge number of attribute in intruder induces a problem in detection process and increase more time in decision making process. In this paper we tried to propose a very simple and fast clustering method for intrusion detection. A hybrid scheme based on coupling two different algorithms one is particle of swarm optimization and other is k-means algorithm. The main originality of proposed approach relies on associating two techniques: extracting more information bits via specific linguistic techniques, space reduction mechanisms, and moreover arcing cluster to aggregate the best clustering result. For the validation and performance evaluation of proposed algorithm used MATLAB software and KDDCUP99 dataset 10%. This dataset contains approx 5 lacks number of instance. The process of result shows that better detection ratio in compare of k-means and k-means-GA technique of intrusion detection.

Keyword: - Feature selection, Intrusion detection system, Genetic Algorithm, Clustering.

Publication Details - 

Title	A Hybrid Model for Intrusion Detection Based on Genetic Clustering and PSO Algorithm
Co-Author	Gaurav Shrivastava**
Publications	International journal of Master of Engineering Research and Technology
Date & Year	SEP 2015
Volume	Vol-2, Issue-9, 2015 Page No. 155-160
ISSN No.	ISSN 2394-6172

Download Full Length Paper

A review on Attribute Selection for Intrusion Detection System with Evolutionary Algorithm

ABSTRACT:- Reduction and selection of intruder attribute in intrusion detection system play an important role in process of detection. The huge number of attribute in intruder induces a problem in detection process and increase more time in decision making process. In this paper we tried to propose a very simple and fast clustering method for intrusion detection. A hybrid scheme based on coupling two different algorithms one is particle of swarm optimization and other is k-means algorithm. The main originality of proposed approach relies on associating two techniques: extracting more information bits via specific linguistic techniques, space reduction mechanisms, and moreover arcing cluster to aggregate the best clustering result. For the validation and performance evaluation of proposed algorithm used MATLAB software and KDDCUP99 dataset 10%. This dataset contains approx 5 lacks number of instance. The process of result shows that better detection ratio in compare of k-means and k-means-GA technique of intrusion detection. 

Keyword: - Feature selection, Intrusion detection system, Genetic Algorithm, Clustering.

Title	A review on Attribute Selection for Intrusion Detection System with Evolutionary Algorithm
Co-Author	Gaurav Shrivastava**
Publications	Engineering Universe for Scientific Research and Management
Date & Year	August 2015
Volume	Vol-7, Issue-8, 2015 Page No. 1-5
ISSN No.	ISSN 2394-6172

Download Full Lenght Paper

A Novel Method of Video Steganography Using Variable LSB

Abstract -

Abstract--Information hiding has become the focus of research today. The term Steganography means “covered writing” and involves transmission of secret messages through apparently innocent files without detection of the fact that message was sent. The innocuous file is known as the cover (video) while the file containing the hidden message referred as Stego medium. Video Steganography is the method of covert some secret data inside a video. In this paper, a novel approach of video Steganography is given which emphasis hiding secure data in video frames.

Keywords - Steganography, covert communication, ANN, FCNN

Publication Details - 

Title	A Novel Method of Video Steganography Using Variable LSB
Co-Author	Gaurav Shrivastava**
Publications	International Journal of Emerging Technology and Advanced Engineering
Date & Year	July 2015
Volume	Volume 5, Issue 7,  Page No. 462-464
ISSN No.	ISSN 2250-2459

Download Full Length Paper

An Approach for Effective Diagnosis of Diseases

Abstract -

In this paper is based on Disease Analysis, as we can say that system is work  as a Artificial Doctor. General Doctor faced the problem about disease  analysis. Patients do not say their symptoms correctly to the doctor because  sometimes they forget to tell and sometimes they are not sincere about the  symptoms. There is one more problem faced by the general medicine doctor  that sometimes they forget to ask some symptoms to the patients, and if they  ask all then this is so time consuming for them to analysis a patient. In  proposed system, we analysis the disease according to the symptoms of patient  and gave the possibility of the diseases. It is like an assistant doctor with more  intelligently 

Keywords -  General Disease Diagnosis, Medical Diagnosis, medical  knowledge

Publication Details - 

Title	An Approach for Effective Diagnosis of Diseases
Co-Author	Gaurav Shrivastava**
Publications	International Journal of Information & Computation Technology.
Date & Year	2014
Volume	Volume 4, Number 16 (2014)  Page No. 1711-1717
ISSN No.	ISSN 0974-2239

Download Full Length Paper

Effective Diagnosis of Diseases through Symptoms Using Artificial Intelligence and Neural Network

Abstract -

In this Research paper is based on artificial intelligence. Artificial Intelligence means learn by knowledge. In this research mechanism for artificial doctor that based on knowledge based. This artificial doctor has the capability to give possibilities of all diseases on the basis of symptoms of patient. It’s like an assistant doctor with more intelligent’s. This mechanism asks the patient about the symptoms. On the basis of those symptoms it will suggest about the possibilities of diseases. This mechanism helps to doctor to identify the disease of the patient. It will also ask about the previous and family history. This mechanism gave the result by studying the previous treatment also, so it takes every possibility of diseases. And it will also alert the doctor for the medicine which cannot be given to the patient.

Keywords-
Artificial Neural Networks, General Disease Diagnosis, Medical Diagnosis, Medical Knowledge, Neural Networks

Publication Details- 

Title	Effective Diagnosis of Diseases through Symptoms Using Artificial Intelligence and Neural Network
Co-Author	Gaurav Shrivastava**
Publications	International Journal of Engineering Research and Applications (IJERA)
Date & Year	Jul-Aug 2013
Volume	Vol. 3, Issue 4, Page No. 2229-2231
ISSN No.	ISSN: 2248-9622

Download Full Length Paper

Using Letters Frequency Analysis in Caesar Cipher with Double Columnar Transposition Technique

Abstract-

In this paper we have some modification in Caesar Cipher Technique. We have proposed a method to enhancing the Caesar cipher for more efficient and secure. We use Relative Frequency of Letters in Alphabets. We arrange the sequence of letter according to the frequency in increasing ordered. And then we have made use of a Modified Caesar cipher technique with double Columnar Transposition Technique.

Keywords-
Caesar Cipher, Double Columnar Transposition Technique, Frequency of Letters.

Publication Details-

Title	Using Letters Frequency Analysis in Caesar Cipher with Double Columnar Transposition Technique
Main Author	Gaurav Shrivastava*
Publications	International Journal Of Engineering Sciences & Research Technology
Date & Year	01 June, 2013
Volume	Vol. 2 Issue 6 ,Page No. 1475-1478
ISSN No.	ISSN: 2277-9655

Download Full Length Paper