Ramzan Mahrukh, Shoaib Muhammad, Altaf Ayesha, Arshad Shazia, Iqbal Faiza, Castilla Ángel Kuc, Ashraf Imran
Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan.
Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain.
Sensors (Basel). 2023 Oct 23;23(20):8642. doi: 10.3390/s23208642.
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.
如今，由于对基于信息技术（IT）的平台和云计算的需求不断增加，网络安全成为一个主要问题。随着互联网的扩展，它一直面临着各种类型的攻击。病毒、拒绝服务（DoS）攻击、分布式拒绝服务（DDoS）攻击、代码注入攻击和欺骗是现代最常见的攻击类型。由于IT的扩展，网络攻击的数量和严重性最近一直在增加。DoS和DDoS是最常报告的网络流量攻击。诸如入侵检测系统和防火墙之类的传统解决方案无法检测复杂的DDoS和DoS攻击。随着基于人工智能的机器学习和深度学习方法的整合，已经提出了几种用于DoS和DDoS检测的新颖方法。特别是，深度学习模型因其卓越的性能在检测DDoS攻击中发挥了关键作用。本研究采用包括递归神经网络（RNN）、长短期记忆（LSTM）和梯度递归单元（GRU）在内的深度学习模型，对最新数据集CICDDoS2019上的DDoS攻击进行检测，并与CICIDS2017数据集进行比较分析。该比较分析有助于开发一种高效且准确的方法来检测DDoS攻击，同时减少执行时间和复杂度。实验结果表明，这些模型在CICDDoS2019数据集上表现相当，准确率得分为0.99，但在执行时间上存在差异，GRU的执行时间比RNN和LSTM的执行时间短。
