Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device

Abstract

Ad-hoc network is a temporary self-organizing network that needs no fixed infrastructure. So it has been applied extensively in many areas requesting temporary communication such as military field, emergency disaster relief and road traffic. While, due to the feature of self-organization and wireless communication channels, ad-hoc network is more vulnerable to various attacks compared to the traditional network. In this paper, we proposed a plug and play device to detect Denial of Service (DoS) and privacy attacks. This device mainly includes capture tool and deep learning detection model. Capture tool is used to grab packets in ad-hoc networks, deep learning detection model is used for detecting attacks. An alarm will be triggered if the detected result is attack. In this way, we can avoid the detected attack to spreading out in larger scale. The proposed method can be used as the second line of dense to issue the early-warning signal. In the experiment, first, we use Deep neural network (DNN) detection model to detect DoS attacks; next, we use DNN, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) detection model to detect XSS and SQL attacks. The results show that these detection models can achieve very high Accuracy, Precision, Recall and F1−score. In addition, the time efficiency among the CNN, the LSTM and the DNN is in acceptable range. It proofs that the proposed method can be effectively applied for attack detection. It is important to note that the proposed method can be extended to all other attacks with little modification in ad-hoc networks.