A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models

Dorieh Alomari; Fatima Anis; Maryam Alabdullatif; Hamoud Aljamaan

doi:10.1145/3593434.3593967

A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models

Dorieh Alomari, Fatima Anis, Maryam Alabdullatif, Hamoud Aljamaan

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Botnets can be a major risk to computer networks, as they attack in dangerous and diverse ways. They are becoming increasingly challenging due to the massive amount of network devices and the obfuscation of communication protocols. This paper provides a critical review and analysis of the recent Machine Learning based models for detecting botnet attacks. It explains the used methodologies, datasets, validation methods, and detection metrics. This paper also identifies the current gaps and limitations to provide recommendations for future research directions in this field. This survey can be used as a guide for new researchers to enhance this research area.

Original language	English
Title of host publication	Proceedings of EASE 2023 - Evaluation and Assessment in Software Engineering
Publisher	Association for Computing Machinery
Pages	493-498
Number of pages	6
ISBN (Electronic)	9798400700446
DOIs	https://doi.org/10.1145/3593434.3593967
State	Published - 14 Jun 2023
Event	27th International Conference on Evaluation and Assessment in Software Engineering, EASE 2023 - Oulu, Finland Duration: 14 Jun 2023 → 16 Jun 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	27th International Conference on Evaluation and Assessment in Software Engineering, EASE 2023
Country/Territory	Finland
City	Oulu
Period	14/06/23 → 16/06/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

Keywords

Botnet
Deep Learning
Detection
Machine Learning
NIDS

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3593434.3593967

Cite this

@inproceedings{8f20a92eb78d48e58a3c38eac2a83008,

title = "A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models",

abstract = "Botnets can be a major risk to computer networks, as they attack in dangerous and diverse ways. They are becoming increasingly challenging due to the massive amount of network devices and the obfuscation of communication protocols. This paper provides a critical review and analysis of the recent Machine Learning based models for detecting botnet attacks. It explains the used methodologies, datasets, validation methods, and detection metrics. This paper also identifies the current gaps and limitations to provide recommendations for future research directions in this field. This survey can be used as a guide for new researchers to enhance this research area.",

keywords = "Botnet, Deep Learning, Detection, Machine Learning, NIDS",

author = "Dorieh Alomari and Fatima Anis and Maryam Alabdullatif and Hamoud Aljamaan",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 27th International Conference on Evaluation and Assessment in Software Engineering, EASE 2023 ; Conference date: 14-06-2023 Through 16-06-2023",

year = "2023",

month = jun,

day = "14",

doi = "10.1145/3593434.3593967",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "493--498",

booktitle = "Proceedings of EASE 2023 - Evaluation and Assessment in Software Engineering",

}

Alomari, D, Anis, F, Alabdullatif, M & Aljamaan, H 2023, A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models. in Proceedings of EASE 2023 - Evaluation and Assessment in Software Engineering. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 493-498, 27th International Conference on Evaluation and Assessment in Software Engineering, EASE 2023, Oulu, Finland, 14/06/23. https://doi.org/10.1145/3593434.3593967

A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models. / Alomari, Dorieh; Anis, Fatima; Alabdullatif, Maryam et al.
Proceedings of EASE 2023 - Evaluation and Assessment in Software Engineering. Association for Computing Machinery, 2023. p. 493-498 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models

AU - Alomari, Dorieh

AU - Anis, Fatima

AU - Alabdullatif, Maryam

AU - Aljamaan, Hamoud

PY - 2023/6/14

Y1 - 2023/6/14

N2 - Botnets can be a major risk to computer networks, as they attack in dangerous and diverse ways. They are becoming increasingly challenging due to the massive amount of network devices and the obfuscation of communication protocols. This paper provides a critical review and analysis of the recent Machine Learning based models for detecting botnet attacks. It explains the used methodologies, datasets, validation methods, and detection metrics. This paper also identifies the current gaps and limitations to provide recommendations for future research directions in this field. This survey can be used as a guide for new researchers to enhance this research area.

AB - Botnets can be a major risk to computer networks, as they attack in dangerous and diverse ways. They are becoming increasingly challenging due to the massive amount of network devices and the obfuscation of communication protocols. This paper provides a critical review and analysis of the recent Machine Learning based models for detecting botnet attacks. It explains the used methodologies, datasets, validation methods, and detection metrics. This paper also identifies the current gaps and limitations to provide recommendations for future research directions in this field. This survey can be used as a guide for new researchers to enhance this research area.

KW - Botnet

KW - Deep Learning

KW - Detection

KW - Machine Learning

KW - NIDS

UR - http://www.scopus.com/inward/record.url?scp=85162238223&partnerID=8YFLogxK

U2 - 10.1145/3593434.3593967

DO - 10.1145/3593434.3593967

M3 - Conference contribution

AN - SCOPUS:85162238223

T3 - ACM International Conference Proceeding Series

SP - 493

EP - 498

BT - Proceedings of EASE 2023 - Evaluation and Assessment in Software Engineering

PB - Association for Computing Machinery

T2 - 27th International Conference on Evaluation and Assessment in Software Engineering, EASE 2023

Y2 - 14 June 2023 through 16 June 2023

ER -

A Survey on Botnets Attack Detection Utilizing Machine and Deep Learning Models

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this