Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms

Berkay Özçam; H. Hakan Kilinc; Abdàl Halim Zaim

doi:10.1109/UBMK52708.2021.9558989

Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms

Berkay Özçam, H. Hakan Kilinc, Abdàl Halim Zaim

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

6 Citations (Scopus)

Abstract

The comfort area created by the fact that people can access everything via the internet has led to an increase in the rate of internet use in recent years. The rise of concepts such as 5G, Internet of Things(IoT), Cloud/Edge/Fog Computing shows that this usage will increase day by day. While this increase brings convenience to humanity, it also increases the appetite of malicious people. Cyber attacks are increasing day by day and many individual or corporate users are harmed. In this study, it is aimed to detect Distributed Denial of Service(DDoS) attacks, which are the most common and most harmful of the bullying we mentioned. We focused on detecting TCP-Flood attacks, which is one of the most preferred DDoS attack types, using various machine learning algorithms. The part that made this job difficult and different was the targeting of real-time detection.

Original language	English
Title of host publication	Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	512-516
Number of pages	5
ISBN (Electronic)	9781665429085
DOIs	https://doi.org/10.1109/UBMK52708.2021.9558989
Publication status	Published - 2021
Externally published	Yes
Event	6th International Conference on Computer Science and Engineering, UBMK 2021 - Ankara, Turkey Duration: 15 Sept 2021 → 17 Sept 2021

Publication series

Name	Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021

Conference

Conference	6th International Conference on Computer Science and Engineering, UBMK 2021
Country/Territory	Turkey
City	Ankara
Period	15/09/21 → 17/09/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE

Keywords

Anomaly Detection
Classification
Clustering
DDoS
Machine Learning
TCP-SYN Flood

Access to Document

10.1109/UBMK52708.2021.9558989

Cite this

Özçam, B., Kilinc, H. H., & Zaim, A. H. (2021). Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms. In Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021 (pp. 512-516). (Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UBMK52708.2021.9558989

Özçam, Berkay ; Kilinc, H. Hakan ; Zaim, Abdàl Halim. / Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms. Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 512-516 (Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021).

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abstract = "The comfort area created by the fact that people can access everything via the internet has led to an increase in the rate of internet use in recent years. The rise of concepts such as 5G, Internet of Things(IoT), Cloud/Edge/Fog Computing shows that this usage will increase day by day. While this increase brings convenience to humanity, it also increases the appetite of malicious people. Cyber attacks are increasing day by day and many individual or corporate users are harmed. In this study, it is aimed to detect Distributed Denial of Service(DDoS) attacks, which are the most common and most harmful of the bullying we mentioned. We focused on detecting TCP-Flood attacks, which is one of the most preferred DDoS attack types, using various machine learning algorithms. The part that made this job difficult and different was the targeting of real-time detection.",

keywords = "Anomaly Detection, Classification, Clustering, DDoS, Machine Learning, TCP-SYN Flood",

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doi = "10.1109/UBMK52708.2021.9558989",

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Özçam, B, Kilinc, HH & Zaim, AH 2021, Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms. in Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021. Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021, Institute of Electrical and Electronics Engineers Inc., pp. 512-516, 6th International Conference on Computer Science and Engineering, UBMK 2021, Ankara, Turkey, 15/09/21. https://doi.org/10.1109/UBMK52708.2021.9558989

Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms. / Özçam, Berkay; Kilinc, H. Hakan; Zaim, Abdàl Halim.
Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 512-516 (Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021).

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

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AU - Zaim, Abdàl Halim

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Y1 - 2021

N2 - The comfort area created by the fact that people can access everything via the internet has led to an increase in the rate of internet use in recent years. The rise of concepts such as 5G, Internet of Things(IoT), Cloud/Edge/Fog Computing shows that this usage will increase day by day. While this increase brings convenience to humanity, it also increases the appetite of malicious people. Cyber attacks are increasing day by day and many individual or corporate users are harmed. In this study, it is aimed to detect Distributed Denial of Service(DDoS) attacks, which are the most common and most harmful of the bullying we mentioned. We focused on detecting TCP-Flood attacks, which is one of the most preferred DDoS attack types, using various machine learning algorithms. The part that made this job difficult and different was the targeting of real-time detection.

AB - The comfort area created by the fact that people can access everything via the internet has led to an increase in the rate of internet use in recent years. The rise of concepts such as 5G, Internet of Things(IoT), Cloud/Edge/Fog Computing shows that this usage will increase day by day. While this increase brings convenience to humanity, it also increases the appetite of malicious people. Cyber attacks are increasing day by day and many individual or corporate users are harmed. In this study, it is aimed to detect Distributed Denial of Service(DDoS) attacks, which are the most common and most harmful of the bullying we mentioned. We focused on detecting TCP-Flood attacks, which is one of the most preferred DDoS attack types, using various machine learning algorithms. The part that made this job difficult and different was the targeting of real-time detection.

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Özçam B, Kilinc HH, Zaim AH. Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms. In Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 512-516. (Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021). doi: 10.1109/UBMK52708.2021.9558989

Detecting TCP Flood DDoS Attack by Anomaly Detection based on Machine Learning Algorithms

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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