eprintid: 9348 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/93/48 datestamp: 2023-10-24 23:30:35 lastmod: 2023-10-24 23:30:37 status_changed: 2023-10-24 23:30:35 type: article metadata_visibility: show creators_name: Ramzan, Mahrukh creators_name: Shoaib, Muhammad creators_name: Altaf, Ayesha creators_name: Arshad, Shazia creators_name: Iqbal, Faiza creators_name: Kuc Castilla, Ángel Gabriel creators_name: Ashraf, Imran title: Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: public keywords: distributed denial of service attacks; denial of service attack detection; deep learning; network security abstract: Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM. date: 2023-10 publication: Sensors volume: 23 number: 20 pagerange: 8642 id_number: doi:10.3390/s23208642 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s23208642 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Abierto Inglés Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM. metadata Ramzan, Mahrukh; Shoaib, Muhammad; Altaf, Ayesha; Arshad, Shazia; Iqbal, Faiza; Kuc Castilla, Ángel Gabriel y Ashraf, Imran mail SIN ESPECIFICAR (2023) Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm. Sensors, 23 (20). p. 8642. ISSN 1424-8220 document_url: http://repositorio.unib.org/id/eprint/9348/1/sensors-23-08642.pdf