Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Computer Applications

Home Area Network(HAN) is a sub system of Local Area Network(LAN) which runs within a limited area or vicinity.In HAN mostly we are running smart grid applications like lights, appliances, network printers etc., by using this network we can also reduce power consumption by sending some controls signals. Intrusion has become one of the big concerns in the Real time. Providing security only to the data processing does not solve the problem. Intrusion are mainly done in three ways 1.Signature-based 2.Anomaly-based 3.Specificationbased, these intrusions can be detected by comparing traffic packet signatures, abnormal behavior of the system, specification of the system. In this paper we are using ZigBee protocol with GSM module for high security. Phase I: Intrusion detection process, it involves an authorized authentication whenever anyone try to intrude the network. Phase II: Intrusion Prevention process it involves GSM module. © Research India Publications.

School of Information Technology and Engineering

Vellore Campus

Department of Software and Systems Engineering

International Journal of Applied Engineering Research

Ids and IPS using ZigBee with GSM

Emergences of GPS enabled phones have made location based services to gain popularity. Location based services like GeoAlerts and Location reminder alarm have grown exponentially over the years. These services utilize the geographical position to alert the user. In our proposed model Geo-Reminder we alert the user with tasks they have to do based on their location. We further develop a Location based Task Scheduler and Reminder Service that builds on existing location aware mechanisms for user centric interactivity. This application is designed specifically for android platform considering the set of API’s. A GPS enabled device need to be empowered with android platform for which Google Maps API will be used in determining the location. This familiar GUI will make the user feel more comfortable alerting the tasks they have to do base on the location. Our application is a follow on member of location-based alert services present in the market such as GeoAlerts, Location Reminder Alarm..etc. But the unique feature that sets it apart from the main stream is its ability to do beyond alerting the user. © Research India Publications.

World University root node

Department of Information Technology

Android based task scheduler and indicator

A wireless sensor network is a organization of a network with a collection of sensor nodes. Every node has some processing capabilities, such as multiple types of memory, and have a some power source, and can accommodate various sensors and actuators. Seaport areas are main points for sea towns, through which much of supplies of world are transported. Now a days, providing security is a global issue at sea-coastal areas, which is main base for communication and commercial terms, Since they are prone to possible terrorist attacks, illegitimate sea piscators, forbidden ships that have felonious intents. We use signal processing and cooperative signal processing (CSP) techniques to distinguish the ship generated waves and ocean generated waves to detect the passing Intrusion Ships (IS). In this paper we propose threearchitecture design to make easy track down of IS. The sink level detection plays an important role to improve the security and surveillance. The Cluster Head Nodes (CHN) are formed by gathering data from environmental changes and processes data to extract the features and signals are passed to surrounding nodes and also CHN has entire knowledge about the network and it can extend security by detecting multiple IS simultaneously. © Research India Publications.

AIMS: Algorithm for intrusion multiple ship detecting system

Association rule mining has been widely adopted by data mining community for discovering relationship among item-sets that co-occur together frequently. Besides positive association rules, negative association rule mining, which find out negation relationships of frequent item-sets are also important. The importance of negative association rule mining is accounted in customer-driven domains such as market basket analysis for identifying products that conflict with each other. In multi-database mining context, mining negation relation among item-sets and synthesizing global negative association rules from multiple data sources located in different places are having importance in arriving decisions both at strategic and branch levels. This paper made an attempt for synthesizing global negative association rules which are voted by most of the participating data sources while mining multiple data sources. Experimental data are employed to test the theoretical analysis of the proposal using UCI machine learning repository data set. The space and time complexity analysis presented in the paper show the efficiency of the proposed approach.

International Arab Journal of Information Technology

Synthesizing global negative association rules in multi-database mining

With increased usage of IT solutions, a huge volume of data is generated from different sources like social networks, CRM, and healthcare applications, to name a few. The size of the data that is generated grows exponentially. As cloud computing provides an optimized, shared, and virtualized IT infrastructure, it is better to leverage the cloud services for storing and processing such Big Data. Securing the data is one of the major challenges in all the domains. Though security and privacy have been talked about for decades, there is still a growing need for high end methods for securing the rampant growth of data. The privacy of personal data, and to be more specific the health data, continues to be an important issue worldwide. Most of the health data in today’s IT world is being computerized. A patient’s health data may portray the different attributes such as his physical and mental health, its severity, financial status, and much more. Moreover, the medical data that are collected from the patients are being shared with other stakeholders of interest like doctors, insurance companies, pharmacies, researchers, and other health care providers. Individuals raise concern about the privacy of their health data in such a shared environment.

Department of Digital Communications

Advances in Data Mining and Database Management Handbook of Research on Cloud Infrastructures for Big Data Analytics

Middleware for Preserving Privacy in Big Data

Security in mobile online banking in present day situation leads to improvise the security of e-commerce. Securing customer transaction information is the crucial issue. So, strong privacy policies are required to authenticate the customer data to prevent thefts and frauds in online transaction of e-payment. Several authentication model were proposed like single factor authentication, two factor authentication using username, password, OTP (One Time Password) which are not completely secure. This paper describes about Multi factor authentication which is a real-time authentication model using biometrics. This eliminates the access of sensitive information of customer by third party at the time of mobile thefts. Authenticating customer details through finger print will be much more secure due to uniqueness of finger print which acts as a password. The proposed algorithm of finger print extraction is Artificial Neural Network using back propagation model. RSA algorithm with Digital Signature will provide enhanced security for the finger print template of customer during transaction between client and server side. This full fills security issues like Data authentication, Integrity, nonrepudiation. © Research India Publications.

Computer Science

School of Computer Science and Engineering

Chennai Campus

Enhancing security in online mobile payments using finger print technology and digital signature

Secure data transfer in Wireless Sensor Networks (WSN) is a challenge, as there are always chances for intruders to form a clone node to interrupt data packets. The proposed system Security in Wireless Sensor Networks by Broadcasting Location Claims (SWBC) engages with Area Based Clustering Detection (ABCD).ABCD divides the WSN into sub areas by implementing division of angle equally and nodes in the sub area elects a Witness node by using maximum neighbour nodes approach. Witness nodes are selected near to the central node to the transmission within the range. Witness nodes gets the location claims of the source node and central node manages the witness nodes where both central node and witness node identifies the intruder by broadcasting the location claims to all the nodes. By simulation SWBC gives the best security compared to the existing techniques like RED, and LSM. Simulation results enhance lifespan of WSNs, decreases the data traffic, detection of intruder before replication of data packets. © 2005 - 2014 JATIT &amp; LLS. All rights reserved.

Journal of Theoretical and Applied Information Technology

Swbc - Security in wireless sensor networks by broadcasting location claims

Now days industries are moving towards technology in order to provide fast and reliable products. Some industries like plants, structures, machinery and shop floors uses wireless sensor network. Wireless sensor networks used for communicating, sensing, monitoring and to process the abilities. Here we are going to discuss about the problem of node replacement in industries. This reliability and security will be done by using harmony search algorithms. We talk about the extraordinary aspects that recognize the industrial facility environment from the rest, expand on security and dependability issues with their particular result measures and we have design a four largescale node to test and optimize the algorithm Hybrid Binary Differential Evolution Harmony Search(HBDEHS). Various tests and fascinating exploration issues have risen up out of this study and have been accounted for further examination. © Research India Publications.

VIT Business School

Security and reliability using wireless sensor network for industrial automation using harmony search algorithm

Now-a-days there is an increase in number of applications that produce large volumes of high dimensional datasets. It is very common that we come across very large sets of observations, represented by hundreds and thousands of input variables in scientific databases. With these large datasets, for example some of the data mining tasks such as clustering, classification, prediction become ineffective. Even when we apply effective data mining algorithms for doing these tasks, results are not satisfactory. Thus, applications on Data mining, Image processing etc., need Dimensionality reduction as first step for solving classification, clustering, prediction problems. It has become a challenge for the researchers to reduce the collected data. There are many statistical Dimension reduction methods used in signal processing, machine learning, data mining applications viz., Factor analysis, Mahalanobis Taguchi method, Singular Value Decomposition, Principal Component Analysis. In this paper, MATLAB program for a simple multivariate statistical model called Principal Component Analysis is written and has been applied to a sample dataset. Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. Step by step explanation for the numerical example has also been presented. Principal Component Analysis is becoming popular now-a-days because of its simplicity. © Research India Publications.

Dimension reduction using multivariate statistical model

In this paper, we propose a framework for automatic topic modeling to connect the relevant topic of study to the appropriate learners in the e-learning system through by suggesting and recommending the hashtags for those topics of interest. In the field of e-learning the impact of social network is highly influenced due to the facilities like learners interaction, portfolio analysis, content analysis, knowledge sharing, collaborative recommendation, content recommendation and etc. The textual content of the messages, blogs, notes and discussion forums can be extracted in a meaningful way for the purpose of making analysis and to recommend the peer learners participating in the elearning management system. In this paper, we investigated the probabilistic, statistical approach based on the latent dirichilet allocation as the topic modeling method. Through this approach, the set of documents and messages are analyzed for generating and suggesting the hashtags as the representation item for the relevant topics of study. In fact, this proposed approach has been tested on the datasets constructed from the e-learning platform and twitter social media network. The effectiveness of the proposed method has been evaluated using the training and testing approach. © Research India Publications.