Classification performance improvement by enhancing the detection accuracy of DDOS attacks over flash crowd using CROSS GAN (XGAN)

Due to the rapid development of network technology in recent years, network security has drawn a lot of attention. Intrusion detection systems are crucial in preventing unwanted traffic from entering networks and computer systems. For network security, it is essential to increase the detection accuracy of network attacks using a range of strategies. However, to increase the detection accuracy of DDoS attacks, the existing model uses a Support vector machine (SVM) and K-Nearest Neighbors (KNN), which does not address the misclassification of data during transmission. Hence, a novel DDoS attack Over Flash Crowd Using Cross GAN (XGAN) has been proposed to classify the performance by enhancing the detection of DDoS attacks in the network which utilizes the information gain, chi-square, and gain ratio to determine the features first using a wrapper-based feature selection ensemble. There is no data collection available right now that has both flash crowd and DDoS sample data. To achieve more accurate categorizations using any classification model, a Generative Adversarial Network (GAN) technique is used to mimic both in the same data set. Then, a Cross Generative Adversarial Network (XGAN), a mixture of two sets of GANs that construct and classify even the imitation damaging attacks with high accuracy, has been provided to improve the detection performance of the model by minimizing the imbalance of attack records. Hence the proposed methodology enhanced the DDoS detection with high accuracy.

Automated summary

With the rapid development of network technology, network security has become a major concern. Intrusion detection systems play a crucial role in preventing unwanted traffic entering networks and computer systems. However, the existing models for detecting DDoS attacks do not address the misclassification of data during transmission. Therefore, a novel classification method has been proposed, called DDoS attack Over Flash Crowd Using Cross GAN (XGAN), which enhances the detection accuracy by utilizing wrapper-based feature selection ensemble and a Cross Generative Adversarial Network (XGAN) to mimic both flash crowd and DDoS attacks.