Classification for SANET Based on Convolutional Neural Networks

Abstract

Attack detection is important for wireless networks and communications generally. Ship ad hoc networks (SANET) are a subset of wireless networks that are vulnerable to denial of service attacks. These attacks are one of the main challenges facing maritime networks, especially dedicated networks because of their weak infrastructure, which makes it easier for these networks to be exposed to this type of attacks. To maintain a secure connection and increase the durability of that connection, an accurate attack detection system must be built. In this paper, we used deep learning algorithm to classify data as either attack or safe. we generated the dataset by building a scenario for the SANET in the network simulator (ns-2). AODV was used as the routing protocol in this simulation, AODV reduces the burden on the network compared with the other protocols (reduces messages flooding in the network). The Convolutional Neural Network (CNN) model were applied to the dataset. The results show that the Convolutional Neural Network have the ability to detect attacks with higher performance. The experimental results showed that the data set that was generated with CNN model as the base classifier produced the best performance in terms of classification precision by 99%.