GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion

Guo, Wei; Qiu, Han; Liu, Zimian; Zhu, Junhu; Wang, Qingxian

doi:10.1155/2022/4611331

Cited by 12 publications

(2 citation statements)

References 59 publications

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“…They have introduced two models employing a straightforward DNN architecture and a Convolutional Autoencoder. The authors demonstrated enhanced classification accuracy through the application of DL techniques, achieving an accuracy of 91.9 Guo et al [39] presented GLD-Net, a DL approach that combines topological and traffic features to achieve high accuracy in detecting DDoS attacks. These investigations collectively illustrate the effectiveness of DL in accurately categorizing DDoS attacks.…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Alqahtani,

Abdullah

2024

IJACSA

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Internet security is under serious threat due to Distributed Denial of Service (DDoS) attacks. These attacks inflict considerable damage by disrupting network services, resulting in the impairment and complete disablement of system functions. The accurate classification and detection of DDoS attacks is extremely important. We provide a review of different models of Machine Learning (ML)/Deep Learning (DL)-based DDoS attack detection used by researchers that consider different classifiers. Our analysis indicates a heightened emphasis on ML-based classifiers where 22% of studies opted for the widely recognized SVM classifier. For DL-based, 27% of the studies opted for the widely recognized CNN. While the majority of researchers have formulated their datasets, NSL-KDD was employed in 55% of the studies. In addition, we discussed the future directions and challenges of DDoS detection.

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Section: B Deep Learning Approachesmentioning

confidence: 99%

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Alqahtani,

Abdullah

2024

IJACSA

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“…With this type of classifier ensures centralized attack detection. In 16 , a topological and flow feature-based deep learning method (GLD-Net) was proposed with the objective of extracting the topological features and also employed graph attention network (GAT) for obtaining correlations between non-Euclidean features. Owing to this the average detection accuracy was said to be improved.…”

Section: Related Workmentioning

confidence: 99%

Sliding principal component and dynamic reward reinforcement learning based IIoT attack detection

Ellappan,

Mahendran,

Subramanian

et al. 2023

Sci Rep

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The Internet of Things (IoT) involves the gathering of all those devices that connect to the Internet with the purpose of collecting and sharing data. The application of IoT in the different sectors, including health, industry has also picked up the threads to augment over the past few years. The IoT and, by integrity, the IIoT, are found to be highly susceptible to different types of threats and attacks owing to the networks nature that in turn leads to even poor outcomes (i.e., increasing error rate). Hence, it is critical to design attack detection systems that can provide the security of IIoT networks. To overcome this research work of IIoT attack detection in large amount of evolutions is failed to determine the certain attacks resulting in a minimum detection performance, reinforcement learning-based attack detection method called sliding principal component and dynamic reward reinforcement learning (SPC–DRRL) for detecting various IIoT network attacks is introduced. In the first stage of this research methodology, preprocessing of raw TON_IoT dataset is performed by employing min–max normalization scaling function to obtain normalized values with same scale. Next, with the processed sample data as output, to extract data from multi-sources (i.e., different service profiles from the dataset), a robust log likelihood sliding principal component-based feature extraction algorithm is applied with an arbitrary size sliding window to extract computationally-efficient features. Finally, dynamic reward reinforcement learning-based IIoT attack detection model is presented to control the error rate involved in the design. Here, with the design of dynamic reward function and introducing incident repository that not only generates the reward function in an arbitrary fashion but also stores the action results in the incident repository for the next training, therefore reducing the attack detection error rate. Moreover, an IIoT attack detection system based on SPC–DRRL is constructed. Finally, we verify the algorithm on the ToN_IoT dataset of University of New South Wales Australia. The experimental results show that the IIoT attack detection time and overhead along with the error rate are reduced considerably with higher accuracy than that of traditional reinforcement learning methods.

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