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

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Abstract

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.

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Abbreviations

Pi :

Probability of class i

N:

Number of attribute values considered within the optimized value ranges

a:

Number of attack classes in the parent node

b:

Number of normal classes in the parent node

ac :

Number of attack classes in the child node

bc :

Number of normal classes in the child node

E(P):

The entropy of the parent node

E(C):

The entropy of the child node

G:

The generator

D:

The discriminator

Pdata(x):

The distribution of real data

P(z):

The distribution of generator

x:

The sample from Pdata(x)

z:

The sample from P(z)

D(x):

The discriminator network

G(z):

The generator network

TP:

True Positive Value

TN:

True Negative Value

FP:

False Positive Value

FN:

False Negative Value

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Authors and Affiliations

  1. Department of Computer Science and Engineering, JNT University, Kakinada, Andhra Pradesh, India

    C H Sekhar & M. H. M. Krishna Prasad

  2. Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, Manchal, Ibrahimpatnam, Telangana, India

    K. Venkata Rao

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  1. C H Sekhar
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  2. K. Venkata Rao
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  3. M. H. M. Krishna Prasad
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Sekhar, C.H., Rao, K.V. & Prasad, M.H.M.K. Classification performance improvement by enhancing the detection accuracy of DDOS attacks over flash crowd using CROSS GAN (XGAN). Multimed Tools Appl 82, 38693–38714 (2023). https://doi.org/10.1007/s11042-023-15151-0

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