DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

DDoS attacks remain a persistent cybersecurity threat, blocking services to legitimate users and causing significant damage to reputation, finances, and potential customers. For the detection of DDoS attacks, machine learning techniques such as supervised learning have been extensively employed, but their effectiveness declines when the framework confronts patterns exterior to the dataset. In addition, DDoS attack schemes continue to improve, rendering conventional data model-based training ineffectual. We have developed a novelty open-set recognition framework for DDoS attack detection to overcome the challenges of traditional methods. Our framework is built on a Convolutional Neural Network (CNN) construction featuring geometrical metric (CNN-Geo), which utilizes deep learning techniques to enhance accuracy. In addition, we have integrated an incremental learning module that can efficiently incorporate novel unknown traffic identified by telecommunication experts through the monitoring process. This unique approach provides an effective solution for identifying and alleviating DDoS. The module continuously improves the model’s performance by incorporating new knowledge and adapting to new attack patterns. The proposed model can detect unknown DDoS attacks with a detection rate of over 99% on conventional attacks from CICIDS2017. The model’s accuracy is further enhanced by 99.8% toward unknown attacks with the open datasets CICDDoS2019.

DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems’ security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack–adversarial DDoS attack. There are established works on ML-based DDoS detection and GAN (Generative Adversarial Network) based adversarial DDoS synthesis. We confirm these findings in our experiments. Experiments in this study involve the extension and application of the GAN, a machine learning framework with symmetric form having two contending neural networks. We synthesize adversarial DDoS attacks utilizing Wasserstein Generative Adversarial Networks featuring Gradient Penalty (GP-WGAN). Experiment results indicate that the synthesized traffic can traverse the detection systems such as k-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Random Forest (RF) without being identified. This observation is a sobering and pessimistic wake-up call, implying that countermeasures to adversarial DDoS attacks are urgently needed. To this problem, we propose a novel DDoS detection framework featuring GAN with Dual Discriminators (GANDD). The additional discriminator is designed to identify adversary DDoS traffic. The proposed GANDD can be an effective solution to adversarial DDoS attacks, as evidenced by the experimental results. We use adversarial DDoS traffic synthesized by GP-WGAN to train GANDD and validate it alongside three other DL technologies: DNN (Deep Neural Network), LSTM (Long Short-Term Memory) and GAN. GANDD outperformed the other DL models, demonstrating its protection with a TPR of 84.3%. A more sophisticated test was also conducted to examine GANDD’s ability to handle unseen adversarial attacks. GANDD was evaluated with adversarial traffic not generated from its training data. GANDD still proved effective with a TPR around 71.3% compared to 7.4% of LSTM.

The network system has become an indispensable component of modern infrastructure. DDoS attacks and their variants remain a potential and persistent cybersecurity threat. DDoS attacks block services to legitimate users by incorporating large amounts of malicious traffic in a short period or depleting system resources through methods specific to each client, causing the victim to lose reputation, finances, and potential customers. With the advancement and maturation of artificial intelligence technology, machine learning and deep learning are widely used to detect DDoS attacks with significant success. However, traditional supervised machine learning must depend on the categorized training sets, so the recognition rate plummets when the model encounters patterns outside the dataset. In addition, DDoS attack techniques continue to evolve, rendering training based on conventional data models unable to meet contemporary requirements. Since closed-set classifiers have excellent performance in cybersecurity and are quite mature, this study will investigate the identification of open-set recognition issues where the attack pattern does not accommodate the distribution learned by the model. This research proposes a framework that uses reconstruction error and distributes hidden layer characteristics to detect unknown DDoS attacks. This study will employ deep hierarchical reconstruction nets (DHRNet) architecture and reimplement it with a 1D integrated neural network employing loss function combined with spatial location constraint prototype loss (SLCPL) as a solution for open-set risks. At the output, a one-class SVM (one-class support vector machine) based on a random gradient descent approximation is used to recognize the unknown patterns in the subsequent stage. The model achieves an impressive detection rate of more than 99% in testing. Furthermore, the incremental learning module utilizing unknown traffic labeled by telecom technicians during tracking has enhanced the model’s performance by 99.8% against unknown threats based on the CICIDS2017 Friday open dataset.

DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a critical component of the creation of an integrated defensive system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers have applied machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have continuously increased and evolved since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric generative adversarial network (GAN) architecture called CycleGAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized attack can easily penetrate ML-based detection systems, including RF (random forest), KNN (k-nearest neighbor), SVM (support vector machine), and naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversarial DDoS attacks. We present a novel DDoS detection framework that incorporates GAN with a symmetrically built generator and discriminator defense system (SDGAN) to deal with these problems. Both symmetric discriminators are intended to simultaneously identify adversarial DDoS traffic. As demonstrated by the experimental results, the suggested SDGAN can be an effective solution against adversarial DDoS attacks. We train SDGAN on adversarial DDoS data generated by CycleGAN and compare it to four previous machine learning-based detection systems. SDGAN outperformed the other machine learning models, with a TPR (true positive rate) of 87.2%, proving its protection ability. Additionally, a more comprehensive test was undertaken to evaluate SDGAN’s capacity to defend against unseen adversarial threats. SDGAN was evaluated using non-training data-generated adversarial traffic. SDGAN remained effective, with a TPR of around 70.9%, compared to RF’s 9.4%.