A New Hybrid Approach for Traffic Identification and Classification in Wireless Networks

Using Ad hoc approach with features such as distributed management between nodes, facilitating their entry and exit into the network and the possibility of better mobility is one of the desirable options for configuration of wireless networks. This sturcture leads to dynamic behavior of the traffic generated by applications in such networks, which affects the issue of network management and traffic control between nodes and this challenge is more important in military networks. Identifying and classifying network traffic can help to deal with these challenges in wireless networks. Because conventional traffic detection and classification methods are not able to provide proper performance with such traffic, the use of machine-learning-based methods can be used to improve the detection and classification performance. In this paper, In this article, the goal is to find a specific network traffic and hence the probability of detection and preventing to make a wrong decision as false alarm rate are introduced which increasing the probability of detection of impermissible traffic is our request. Therefore, in this paper a new hybrid method, based on the combination of machine learning methods is introduced to increase accuracy and efficiency in identifying and classifying traffic in the wireless network based on wireless traffic dataset. The results show that the proposed method improve the detection of a special target without considerable increase in false alarm rate, compared to the case of employing unique machine learning methods.