I nternational Journal of Application or I nnovation in Engineering& Management (I JAI EM) Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com Volume 2, Issue 7, July 2013 ISSN 2319 - 4847 Volume 2, Issue 7, July 2013 Page 249 ABSTRACT Mobile Ad Hoc Networks has more susceptible towards vulnerabilities compared with wired networks. MANET has become an important technology in current years because of the rapid explosion of wireless devices. They are highly susceptible to attacks due to the open medium, dynamically changing network topology. MANETs have unique characteristics like dynamic topology, wireless radio medium, limited resources and lack of centralized administration; as a result, they are vulnerable to different types of attacks in different layers of protocol stack. Each node in a MANET is capable of acting as a router. Routing is one of the aspects having various security concerns. There are various common Denial-of-Service (DoS) attacks occurs on network layer namely Wormhole attack, Blackhole attack and Grayhole attack which are serious threats for MANETs. It is required to search new architecture and mechanisms to protect these networks. With continuous scale-up of the network and increase of the kinds of the services on the network, more and more people pay attention to the modeling and prediction for network traffic. Recently, SVM Support Vector Machine), a new machine learning method, is comprehensively used to solve the problem of non-liner classification and regression. Support vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non- probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. A network traffic predictive method presented in this paper is based on the LS-SVM (Least Squares SVM). In machine learning, the (Gaussian) radial basis function kernel, or RBF kernel, is a popular kernel function. It is the most popular kernel function used in support vector machine classification. The RBF kernel on two samples x and x', represented as feature vectors in some input space. A network traffic predictive method presented in this paper is based on the SVM. Using NS2 simulator, we simulate the process of the network. Keywords: Mobile Ad-hoc network, Routing protocols, Support Vector Machine, Machine classifier, Denial of Services. 1. INTRODUCTION Mobile Ad-Hoc networks are famous as network of interconnected devices, independent of any predefined motion. Nodes are free to move due to their nature of communication. In MANET, communication is performed without the help of fixed infrastructure like other networks. Growing number of mobile and wireless devices will help to become MANET more famous. These mobile devices require a movable environment according to their nature. It leads to researcher to focus on more advance feature in MANET. The explosion of wireless devices in mobile ad-hoc networks and their use in critical scenarios like communications in war require new safety mechanisms and policies to promise the reliability, privacy and accessibility of the data transmitted. Mobile Ad Hoc Networks has more challenge compared with wired networks. Mobile ad hoc networking (MANET) has turn out to be an important expertise in present time because of the rapid increase in number of wireless devices. They are highly susceptible to attacks due to the open Figure 1 Mobile Ad-hoc network An Effective DoS Prevention System to Analysis and Prediction of Network Traffic Using Support Vector Machine Learning Anil Kumar Sharma 1 , Pankaj Singh Parihar 2 1 M.Tech Scholar (CSE), Institute of Technology & Management, Bhilwara 2 Assistant Professor, Institute of Technology & Management, Bhilwara