Open AccessDeep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET
Author(s) -
Polat Huseyin,
Turkoglu Muammer,
Polat Onur
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2020.0477
Subject(s) - denial of service attack , computer science , autoencoder , softmax function , vehicular ad hoc network , computer network , deep learning , classifier (uml) , artificial intelligence , wireless ad hoc network , machine learning , computer security , the internet , telecommunications , world wide web , wireless
Software‐defined network (SDN)‐based vehicular ad hoc network (VANET) is an outstanding technology for smart transportation as it increases traffic safety, efficiency, comfort, and manageability. However, despite all its benefits and good performance, SDN‐based VANET is vulnerable to attack threats such as distributed denial of service (DDoS). When SDN‐based VANET systems are exposed to DDoS attacks, this may affect traffic safety, causing traffic accidents and deaths. Therefore, the relevant security threats need to be addressed before integrating the SDN‐based VANETs into smart transportation systems. In this study, the stacked sparse autoencoder (SSAE) + Softmax classifier deep network model is proposed to detect DDoS attacks targeting SDN‐based VANETs. The features in the dataset obtained from the SDN‐based VANET were reduced dimensionally utilising SSAE, and the most significant features were obtained. Then, these features were used as input into the Softmax classifier. According to the experimental results, the best accuracy scores were calculated as 96.9% using the four‐layer SSAE + Softmax classifier deep network model proposed. When compared, the results demonstrate the SSAE + Softmax classifier deep network model proposed can obtain better results in the classification of DDoS attacks and is more successful than the other machine learning classifiers.