Search

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. [ABSTRACT FROM AUTHOR]

DDoS (distributed denial of service) attack is a constant and serious danger to the Internet. New application-layer-based DDoS attacks that use valid requests to overload target resources are more undetected than low-layer DDoS attacks. When flash crowd assaults imitate or occur during a flash crowd event on webservers, the situation may become more dangerous. Flash Crowd Assaults (FCAs) are DDoS attacks that flood victim services, such as Web servers, with well-formed requests created by a large number of bots. Because both valid and attack requests appear identical, it's difficult to identify and filter such attacks.Hence, differentiating DDoS and FC is a critical task in protecting web servers against attacks, which can be fatal to cyber-systems.In this article, a novel Deep Neural Network empowered Bi-Directional Cross Generative Adversarial Network (GAN) is introduced for recognizing and separating distributed denial-of-service attacks from flash crowd attacks over web applications. The suggested research uses an ensemble feature selection approach to construct real samples of attack data in order to accomplish the goal. In the meanwhile, the generator network uses a bidirectionally cross GAN to generate bogus attack data by observing the random noise vector as input. The discriminator receives both manual and model input and using DNN to distinguish between DDoS assaults and flash crowd attacks. The proposed model is implemented in the working platform of python and is tested using the evaluation metrics like accuracy, precision, recall, and f1-measure.From the implementation result, is evident that the proposed model achieves an effective performance in whichthe obtained accuracy of the proposed approach is 96.58% which is comparatively higher than the existing techniques. [ABSTRACT FROM AUTHOR]