The quick growth of mobile communication in current years is especially observed in the field of mobile system, wireless local area network, and ubiquitous computing. In this vast of time, ad-hoc networks are extremely active networks characterized by the lack of physical infrastructure. Hubs of this system's capacity as switch which find and keeps up the route to different hubs in the system. Therefore, for ad-hoc network, routing protocol plays a significant task for building an efficient network. The plan of efficient routing protocols is a basic problem in a Mobile Ad-Hoc Network (MANET). Lots of different protocols have been proposed in the literature, each one based on dissimilar characteristics and properties. In this paper, an attempt has been complete to compare three well known routing protocols Ad-hoc on demand distance vector (AODV), Dynamic Source Routing (DSR) and Destination Sequenced Distance Vector (DSDV) by using three presentation metrics packet delivery ratio (PDR), Routing Overhead and Remain Energy by using the NS-2.34 simulator.

The data mining is used in various applications in order to provide decisions, pattern matching and others. In order to mine data, it is required to initially learn from the data and then extract the patterns from data which is supplied in raw formats. Sometimes data is available in parts and need to conclude the outcomes from the data in secure manner. Therefore a technique is required that securely combine data from all the participating partitions of the data. Mine the combined data for extracting the meaningful pattern in a secured manner. Finally disclose the data in such manner which is utilized by the different other application, without disturbing the outcomes of the final application decisions. In order to design and demonstrate the three parties of data are considered for combining them, and mine them. And finally using the C4.5 classification algorithm is applied to find the final utilization on different application. To find that the mining decision is not varied from the initial values of mining the comparison is made between the disclosed data and initial dataset on the basis of accuracy of classification. The experimental results demonstrate a very fewer patterns are misclassified after in comparison of the initial dataset classification in terms of accuracy.

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IP address spoofing is a serious threat to the legitimate use of the Internet. Many Preventive mechanisms are thwarted by the ability of attackers to forge or spoof the source addresses in IP packets. Attackers can evade detection and put a substantial burden on the destination network for policing attack packets. In this paper a study of methods for detection of IP address spoofing is undertaken. It compares various host based methods such as IPSec, the OS