Detection of DDoS attacks with feed forward based deep neural network model

• Published in: Expert systems with applications Vol. 169; p. 114520
• Main Authors: Cil, Abdullah Emir, Yildiz, Kazim, Buldu, Ali
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2021; Elsevier BV
• Subjects: Accuracy; Artificial neural networks; Communications traffic; Cybersecurity; DDoS attack; Deep learning; Denial of service attacks; DNN model; Feature extraction; Machine learning; Network traffic; Neural networks; Security management; Traffic models; Deep learning; DDoS attack; DNN model; Network traffic
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.114520

•Small samples from DDoS data set are used to obtain fast and effective results.•Model with feature extraction and classification is chosen to better accuracy.•DDoS attacks in network are detected and classified using deep neural network.•Performance of proposed models is evaluated on current DDoS attacks dataset. As a result of the increase in the services provided over the internet, it is seen that the network infrastructure is more exposed to cyber attacks. The most widely used of these attacks are Distributed Denial of Service (DDoS) attacks that easily disrupt services. The most important factor in the fight against DDoS attacks is the early detection and separation of network traffic. In this study, it is suggested to use the deep neural network (DNN) as a deep learning model that detects DDoS attacks on the sample of packets captured from network traffic. DNN model can work quickly and with high accuracy even in small samples because it contains feature extraction and classification processes in its structure and has layers that update itself as it is trained. As a result of the experiments carried out on the CICDDoS2019 dataset containing the current DDoS attack types created in 2019, it was observed that the attacks on network traffic were detected with 99.99% success and the attack types were classified with an accuracy rate of 94.57%. The high accuracy values obtained show that the deep learning model can be used effectively in combating DDoS attacks.