Intrusion Detection and Performance Analysis Using Copula Functions

Mehmet Burukanlı; Musa Çıbuk

Article Details

Intrusion Detection and Performance Analysis Using Copula Functions

Authors:

Mehmet Burukanlı | Musa Çıbuk

Publisher:

Bitlis Eren University

Main Topics:

Artificial Intelligence (Other) | Statistics (Other)

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 4, pp.1335-1354

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001336504

pISSN:

2147-3129

Keywords:

Abstract:

 Nowadays, interest in technology is growing as technology advances and makes our jobs easier. These rapid technological advancements bring with them a slew of unwanted negative attacks, such as cyber-attacks and unauthorized access. To prevent such negative attacks, intrusion detection systems are frequently used. In this research, we make some suggestions for novel and reliable classifiers for intrusion detection systems that are based on copulas. Using copula-based classifiers, we hope to detect intrusion in computer networks. Students-t, Gumbel, Clayton, Gaussian, Independent and Frank classifiers, which are frequently used in the literature, have been preferred as copula-based classifiers. These classifiers were used to perform classification on the KDD99 dataset. The 10-fold cross-validation method has been used in the classification phase. When the experimental results were examined, the proposed Gaussian copula-based classifier outperformed state-of-the-art basic methods on the KDD99 dataset with a success rate of 99.41. As a direct consequence of this, classifiers based on the copula have shown promising results in the field of intrusion detection. Classifiers that are based on the copula have been found to be a competitive alternative to the most recent and cutting-edge fundamental approaches.

 Nowadays, interest in technology is growing as technology advances and makes our jobs easier. These rapid technological advancements bring with them a slew of unwanted negative attacks, such as cyber-attacks and unauthorized access. To prevent such negative attacks, intrusion detection systems are frequently used. In this research, we make some suggestions for novel and reliable classifiers for intrusion detection systems that are based on copulas. Using copula-based classifiers, we hope to detect intrusion in computer networks. Students-t, Gumbel, Clayton, Gaussian, Independent and Frank classifiers, which are frequently used in the literature, have been preferred as copula-based classifiers. These classifiers were used to perform classification on the KDD99 dataset. The 10-fold cross-validation method has been used in the classification phase. When the experimental results were examined, the proposed Gaussian copula-based classifier outperformed state-of-the-art basic methods on the KDD99 dataset with a success rate of 99.41. As a direct consequence of this, classifiers based on the copula have shown promising results in the field of intrusion detection. Classifiers that are based on the copula have been found to be a competitive alternative to the most recent and cutting-edge fundamental approaches.

 Nowadays, interest in technology is growing as technology advances and makes our jobs easier. These rapid technological advancements bring with them a slew of unwanted negative attacks, such as cyber-attacks and unauthorized access. To prevent such negative attacks, intrusion detection systems are frequently used. In this research, we make some suggestions for novel and reliable classifiers for intrusion detection systems that are based on copulas. Using copula-based classifiers, we hope to detect intrusion in computer networks. Students-t, Gumbel, Clayton, Gaussian, Independent and Frank classifiers, which are frequently used in the literature, have been preferred as copula-based classifiers. These classifiers were used to perform classification on the KDD99 dataset. The 10-fold cross-validation method has been used in the classification phase. When the experimental results were examined, the proposed Gaussian copula-based classifier outperformed state-of-the-art basic methods on the KDD99 dataset with a success rate of 99.41. As a direct consequence of this, classifiers based on the copula have shown promising results in the field of intrusion detection. Classifiers that are based on the copula have been found to be a competitive alternative to the most recent and cutting-edge fundamental approaches.

 Nowadays, interest in technology is growing as technology advances and makes our jobs easier. These rapid technological advancements bring with them a slew of unwanted negative attacks, such as cyber-attacks and unauthorized access. To prevent such negative attacks, intrusion detection systems are frequently used. In this research, we make some suggestions for novel and reliable classifiers for intrusion detection systems that are based on copulas. Using copula-based classifiers, we hope to detect intrusion in computer networks. Students-t, Gumbel, Clayton, Gaussian, Independent and Frank classifiers, which are frequently used in the literature, have been preferred as copula-based classifiers. These classifiers were used to perform classification on the KDD99 dataset. The 10-fold cross-validation method has been used in the classification phase. When the experimental results were examined, the proposed Gaussian copula-based classifier outperformed state-of-the-art basic methods on the KDD99 dataset with a success rate of 99.41. As a direct consequence of this, classifiers based on the copula have shown promising results in the field of intrusion detection. Classifiers that are based on the copula have been found to be a competitive alternative to the most recent and cutting-edge fundamental approaches.

Article Details

Intrusion Detection and Performance Analysis Using Copula Functions

Authors:

Mehmet Burukanlı | Musa Çıbuk

Publisher:

Bitlis Eren University

Main Topics:

Artificial Intelligence (Other) | Statistics (Other)

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 4, pp.1335-1354

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001336504

pISSN:

2147-3129

Intrusion Detection

Copula Functions

KDD’99 Dataset

Naive Bayes Classifier

mRMR

Issues

Article Details

Intrusion Detection and Performance Analysis Using Copula Functions

Authors: Mehmet Burukanlı | Musa Çıbuk

Publisher: Bitlis Eren University

Main Topics: Artificial Intelligence (Other) | Statistics (Other)

Journal: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 4, pp.1335-1354

Document Type: Article

Publication Date: 12/2024

Document Language: English

Document ID (Org No.): 2001336504

pISSN: 2147-3129

Intrusion Detection

Copula Functions

KDD’99 Dataset

Naive Bayes Classifier

mRMR

Issues

Authors:

Mehmet Burukanlı | Musa Çıbuk

Publisher:

Bitlis Eren University

Main Topics:

Artificial Intelligence (Other) | Statistics (Other)

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 4, pp.1335-1354

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001336504

pISSN:

2147-3129