Improved Hybrid Algorithms using Data Mining Techniques for Intrusion Detection System

The tremendous growth of the usage of computers over network and development in application running on a various platform captures the attention towards network security. This paradigm exploits security vulnerabilities on all computer systems that are  technically difficult and expensive to be solved.  As a result, Intrusion Detection Systems (IDSs), are widely used to detect and stop network intrusions [1,2]. An intrusion detection system watches networked devices & searches for malicious behaviors in kinds of pattern in the audit stream [13]. One main conflict in intrusion detection [3] is that we have to find out the hidden attacks from a large quantity of routine communication activities. The security of our computer systems & data is at continual risks due to the extensive growth of the internet & increasing availability of tools & tricks for intruding & attacking networks have made intrusion detection to become a critical component of network administration. In traditional intrusion detection systems,  the network administrator needs to spend much time in analyzing the data and also generates high false alarm rates.  But data mining based intrusion detection systems are more precise, require less manual processing time and input from human experts.

G.V.Nadiammai

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DYNAMIC FAULT TOLERANT DEPENDENCY SCHEDULING ALGORITHM FOR QUERY SCHEDULING IN DISTRIBUTED DATA WAREHOUSE

Data receiving capacity, storage, process time and availability relates to the organization’s success. Structured collection of data is called as database. In computing, many different operational databases are stored in a central repository known as a data warehouse accessed by reporting and data analysis applications. The volume of data will be distributed over multiple processors called as a technologically distributed data warehouse. This type of warehouses usually involves the most redundant data and complex loading and updating processes. If the volume of the data increased in the limit of a distributed processor, the other processor can be added to the network. As the amount of data and number of sites grows, often distributed system becomes crucial for scheduling the queries. The query scheduling process is very compact to accomplish these tasks within a few seconds. So here gird based task and resource scheduling algorithms have been used to resolve these issues.

KRISHNAVENI S 

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Energy Optimization of Virtual Machines Placement in Cloud Data Centres ( UK )

Cloud computing is an Internet Cloud, a technology emerging and advancing rapidly in Information and Communication Technology (ICT) attributed to its flexibility of using computing resources on a pay-as-you-go basis. Computing resources are delivered on-demand over the Internet. Some of the services include: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS), and all these services can be deployed either in public, private or hybrid model. Today data centres are highly configured with advanced servers supporting such kind of trends in computing. Servers in data centres remain idle when not in use. This server can be switched off or put in sleep mode to reduce energy consumption [1]. The Infrastructure as a Service (IaaS) layer is the pool of computing resources, storage  and network fabrics, the fast becoming services in cloud computing, it provides computing resources for small to large scale. Services like computer resource (Amazon EC2), database, and storage (Amazon S3) are some of the services which are widely used. An API (Application Programming Interface) is used to access the infrastructure with a dashboard to control the server and to create and manage different Virtual Machines (VM) and other services in the data centre.

NONGMAITHEM AJITH SINGH

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Energy Optimization of Virtual Machines Placement in Cloud Data Center

Cloud computing is an Internet Cloud, a technology emerging and advancing rapidly in Information and Communication Technology (ICT) attributed to its flexibility of using computing resources on a pay-as-you-go basis. Computing resources are delivered on-demand over the Internet. Some of the services include: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS), and all these services can be deployed either in public, private or hybrid model. Today data centres are highly configured with advanced servers supporting such kind of trends in computing. Servers in data centres remain idle when not in use. This server can be switched off or put in sleep mode to reduce energy consumption [1]. The Infrastructure as a Service (IaaS) layer is the pool of computing resources, storage  and network fabrics, the fast becoming services in cloud computing, it provides computing resources for small to large scale. Services like computer resource (Amazon EC2), database, and storage (Amazon S3) are some of the services which are widely used. An API (Application Programming Interface) is used to access the infrastructure with a dashboard to control the server and to create and manage different Virtual Machines (VM) and other services in the data centre.

NONGMAITHEM AJITH SINGH

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