Botnets are the networks of remotely controlled computer systems infected with a malicious program that allow cyber crimes to control the infected computers or machines without the user’s knowledge. Botnets are the most ever-growing much interested evolved in the design of mobile adhoc networks (MANET). A botnet in mobile network is defined as a collection of nodes containing a malware called mobile malware which are able to bring the different elements into harmonious activities. Unlike Internet botnets, mobile botnets do not need to propagate using centralized structure. With the advent of internet and e-commerce application data security is the most critical issue in transferring the information throughout the internet. Botnets are emerging as the most significant threat facing computing assets and online ecosystems. The sharing of information through internet has been the main driver behind the Elite hacker into criminal activities. Their main target is to steal the vulnerable information from the individuals or from the organizations. In other words we can say their purpose include the distribution of spam emails, coordination of distributed denial of service (DDoS) and automatic identity theft. The proposed method is a classified model in which a Hill Cipher Algorithm and a Support Vector Machine are combined. A MANET environment with real time datasets is simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of Artificial Fish Swarm Algorithm.