In the past few years, cybersecurity is becoming very important due to the rise in internet users. The internet attacks such as Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks severely harm a website or server and make them unavailable to other users. Network Monitoring and control systems have found it challenging to identify the many classes of DoS and DDoS attacks since each operates uniquely. Hence a powerful technique is required for attack detection. Traditional machine learning techniques are inefficient in handling extensive network data and cannot extract high-level features for attack detection. Therefore, an effective deep learning-based intrusion detection system is developed in this paper for DoS and DDoS attack classification. This model includes various phases and starts with the Deep Convolutional Generative Adversarial Networks (DCGAN) based technique to address the class imbalance issue in the dataset. Then a deep learning algorithm based on ResNet-50 extracts the critical features for each class in the dataset. After that, an optimized AlexNet-based classifier is implemented for detecting the attacks separately, and the essential parameters of the classifier are optimized using the Atom search optimization algorithm. The proposed approach was evaluated on benchmark datasets, CCIDS2019 and UNSW-NB15, using key classification metrics and achieved 99.37% accuracy for the UNSW-NB15 dataset and 99.33% for the CICIDS2019 dataset. The investigational results demonstrate that the suggested approach performs superior to other competitive techniques in identifying DoS and DDoS attacks.

翻译：近年来，随着互联网用户数量的增长，网络安全变得日益重要。拒绝服务（DoS）攻击和分布式拒绝服务（DDoS）攻击等网络攻击会严重破坏网站或服务器，使其无法为其他用户提供服务。由于各类DoS和DDoS攻击的攻击方式各不相同，网络监控与控制系统难以有效识别这些攻击类别，因此亟需一种强大的攻击检测技术。传统机器学习方法在处理大规模网络数据时效率低下，且无法提取用于攻击检测的高层特征。为此，本文提出了一种基于深度学习的有效入侵检测系统，用于DoS和DDoS攻击分类。该模型包含多个阶段：首先，采用基于深度卷积生成对抗网络（DCGAN）的方法解决数据集中的类别不平衡问题；其次，基于ResNet-50的深度学习算法提取数据集中每个类别的关键特征；然后，实现了一种优化的基于AlexNet的分类器，用于单独检测各类攻击，并利用原子搜索优化算法对分类器的关键参数进行优化。所提方法在基准数据集CCIDS2019和UNSW-NB15上，使用关键分类指标进行评估，在UNSW-NB15数据集上达到99.37%的准确率，在CICIDS2019数据集上达到99.33%的准确率。实验结果表明，所提方法在识别DoS和DDoS攻击方面优于其他竞争技术。