Biblio

Mobile ad-hoc network (MANET) contains various wireless movable nodes which can communicate with each other and they don't require any centralized administrator or network infrastructure and also can communicate with full capacity because it is composed of mobile nodes. They transmit data to each other with the help of intermediate nodes by establishing a path. But sometime malicious node can easily enter in network due to the mobility of nodes. That malicious node can harm the network by dropping the data packets. These type of attack is called gray hole attack. For detection and prevention from this type of attack a mechanism is proposed in this paper. By using network simulator, the simulation will be carried out for reporting the difficulties of prevention and detection of multiple gray hole attack in the Mobile ad-hoc network (MANET). Particle Swarm Optimization is used in this paper. Because of ad-hoc nature it observers the changing values of the node, if the value is infinite then node has been attacked and it prevents other nodes from sending data to that node. In this paper, we present possible solutions to prevent the network. Firstly, find more than one route to transmit packets to destination. Second, we provide minimum time delay to deliver the packet. The simulation shows the higher throughput, less time delay and less packet drop.

The proliferation of mobile technology promotes social activities without time and space limitation. Users share information about their interests and preferences through a social network service, blog, or community. However, sensitive personal information may be exposed with the use of social activities. For example, a specific person can be identified according to exposure of personal information on the web. In this paper, we shows that a nickname that is used in an online community can be tracked by analysis of a user's behavior even though the nickname is changed to avoid identification. Unlike existing studies about user identification in a social network service, we focus on online community, which has not been extensively studied. We analyze characteristics of the online community and propose a method to track a user's nickname change to identify the user. We validate the proposed method using data collected from the online community. Results show that the proposed method can track the user's nickname change and link the old nickname with the new one.