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Wireless and mobile sensor network technologies in M2M (machine to machine) are rapidly applied to our real life. Thus, in near future, advanced wireless and mobile sensor network in M2M application will be major key factor of the future generation convergence network which is based on the state of the application. It is expected that smart machines will appear as new business service model with other machines. Most numerous researches within M2M sectors are carried out in intelligent vehicle sector integrated with IT technology. Intelligent vehicle section shows severe changes in position between vehicles and has numerous large scales of networks in its components; therefore, it is required to provide safety by exchanging information between vehicles equipped with wireless communication function via VANET (vehicular ad hoc network) and fixed apparatus at roadside regarding the status of road. In this paper, we proposed cluster authentication scheme that mutually authenticates between vehicles by composing vehicle movement as cluster configuration architecture. We have successfully included the establishment of secure channels, the detection of replay attacks, mutual cluster authentication, prevention of vehicle identity fabrication, and secure distribution of provisional session key.

As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure. [ABSTRACT FROM AUTHOR]