Analysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithms

Fuat Türk

Makale detayları

Analysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithms

Yazarlar:

Fuat Türk

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 2, pp.465-477

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292735

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of smart devices in almost every field, the provision of services by private and public institutions over network servers, cloud technologies and database systems are almost completely remotely controlled. Due to these increasing requirements for network systems, malicious software and users, unfortunately, are increasing their interest in these areas. Some organizations are exposed to almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and correct analysis of network attacks is vital for the operation of the entire system. With deep learning and machine learning, attack detection and classification can be done successfully. In this study, a comprehensive attack detection process was performed on UNSW-NB15 and NSL-KDD datasets with existing machine learning algorithms. In the UNSW-NB115 dataset, 98.6 and 98.3 accuracy were obtained for two-class and multi-class, respectively, and 97.8 and 93.4 accuracy in the NSL-KDD dataset. The results prove that machine learning algorithms are lateral to the solution in intrusion detection systems.

 Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of smart devices in almost every field, the provision of services by private and public institutions over network servers, cloud technologies and database systems are almost completely remotely controlled. Due to these increasing requirements for network systems, malicious software and users, unfortunately, are increasing their interest in these areas. Some organizations are exposed to almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and correct analysis of network attacks is vital for the operation of the entire system. With deep learning and machine learning, attack detection and classification can be done successfully. In this study, a comprehensive attack detection process was performed on UNSW-NB15 and NSL-KDD datasets with existing machine learning algorithms. In the UNSW-NB115 dataset, 98.6 and 98.3 accuracy were obtained for two-class and multi-class, respectively, and 97.8 and 93.4 accuracy in the NSL-KDD dataset. The results prove that machine learning algorithms are lateral to the solution in intrusion detection systems.

 Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of smart devices in almost every field, the provision of services by private and public institutions over network servers, cloud technologies and database systems are almost completely remotely controlled. Due to these increasing requirements for network systems, malicious software and users, unfortunately, are increasing their interest in these areas. Some organizations are exposed to almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and correct analysis of network attacks is vital for the operation of the entire system. With deep learning and machine learning, attack detection and classification can be done successfully. In this study, a comprehensive attack detection process was performed on UNSW-NB15 and NSL-KDD datasets with existing machine learning algorithms. In the UNSW-NB115 dataset, 98.6 and 98.3 accuracy were obtained for two-class and multi-class, respectively, and 97.8 and 93.4 accuracy in the NSL-KDD dataset. The results prove that machine learning algorithms are lateral to the solution in intrusion detection systems.

 Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of smart devices in almost every field, the provision of services by private and public institutions over network servers, cloud technologies and database systems are almost completely remotely controlled. Due to these increasing requirements for network systems, malicious software and users, unfortunately, are increasing their interest in these areas. Some organizations are exposed to almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and correct analysis of network attacks is vital for the operation of the entire system. With deep learning and machine learning, attack detection and classification can be done successfully. In this study, a comprehensive attack detection process was performed on UNSW-NB15 and NSL-KDD datasets with existing machine learning algorithms. In the UNSW-NB115 dataset, 98.6 and 98.3 accuracy were obtained for two-class and multi-class, respectively, and 97.8 and 93.4 accuracy in the NSL-KDD dataset. The results prove that machine learning algorithms are lateral to the solution in intrusion detection systems.

Makale detayları

Analysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithms

Yazarlar:

Fuat Türk

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 2, pp.465-477

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292735

pISSN:

2147-3129

UNSW-NB15 dataset

NSL-KDD dataset

Instruction Detection Systems

Machine Learning

Network AttackUNSW-NB15 dataset

NSL-KDD dataset

Instruction Detection Systems

Machine Learning

Network Attack

Sayı

Makale detayları

Yazarlar: Fuat Türk

Yayıncı: Bitlis Eren University

Ana Başlıklar: Engineering

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 2, pp.465-477

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001292735

pISSN: 2147-3129

Sayı

Yazarlar:

Fuat Türk

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 2, pp.465-477

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292735

pISSN:

2147-3129