Classification Methods of Machine Learning to Detect DDoS Attacks

Радівілова, Тамара; Kirichenko, Lyudmyla; Ageiev, Dmytro; Bulakh, Vitalii

doi:10.1109/idaacs.2019.8924406

Cited by 30 publications

(10 citation statements)

References 14 publications

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“…[20], the ANN demonstrated the highest accuracy compared to the other two methods for the generated dataset. Radivilova et al [21] utilized the SNMP-MIB Dataset to investigate various types of attacks, including TCP SYN, UDP flood, ICMPECHO, HTTP flood, Slowpost, Slowloris, and SSH brute force. The study involved analyzing primary approaches for detecting DDoS attacks through the realization of network traffic.…”

Section: A Machine Learning Approachesmentioning

confidence: 99%

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Alqahtani,

Abdullah

2024

IJACSA

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Internet security is under serious threat due to Distributed Denial of Service (DDoS) attacks. These attacks inflict considerable damage by disrupting network services, resulting in the impairment and complete disablement of system functions. The accurate classification and detection of DDoS attacks is extremely important. We provide a review of different models of Machine Learning (ML)/Deep Learning (DL)-based DDoS attack detection used by researchers that consider different classifiers. Our analysis indicates a heightened emphasis on ML-based classifiers where 22% of studies opted for the widely recognized SVM classifier. For DL-based, 27% of the studies opted for the widely recognized CNN. While the majority of researchers have formulated their datasets, NSL-KDD was employed in 55% of the studies. In addition, we discussed the future directions and challenges of DDoS detection.

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Section: A Machine Learning Approachesmentioning

confidence: 99%

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Alqahtani,

Abdullah

2024

IJACSA

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“…One of the techniques is the Decision Tree, which is susceptible to overfitting, susceptible to noise in the dataset, and unsuitable for large datasets [23]. An experiment has been conducted in which brute force attacks are identified using a model based on processing the network log to obtain information by reading certain alerts in the network log, such as "SSH user failed to login from IP," to determine that the IP is executing a brute force attack [12]. Another experiment classifies the brute-force attack using deep learning.…”

Section: Related Studiesmentioning

confidence: 99%

Comparing FTP and SSH Password Brute Force Attack Detection using k-Nearest Neighbour (k-NN) and Decision Tree in Cloud Computing

Kamarudin Shah,

Md-Arshad,

Abdul Samad

et al. 2023

Int J Innov Comp

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Cloud computing represents a new epoch in computing. From huge enterprises to individual use, cloud computing always provides an answer. Therefore, cloud computing must be readily accessible and scalable, and customers must pay only for the resources they consume rather than for the entire infrastructure. With such conveniences, come with their own threat especially brute force attacks since the resources are available publicly online for the whole world to see. In a brute force attack, the attacker attempts every possible combination of username and password to obtain access to the system. This study aims to examine the performance of the k-Nearest Neighbours (k-NN) and Decision Tree algorithms by contrasting their precision, recall, and F1 score. This research makes use of the CICIDS2017 dataset, which is a labelled dataset produced by the Canada Institute for Cybersecurity. A signature for the brute force attack is utilised with an Intrusion Detection System (IDS) to detect the attack. This strategy, however, is ineffective when a network is being attacked by a novel or unknown attack or signature. At the conclusion of the study, the performance of both algorithms is evaluated by comparing their precision, recall, and f1 score. The results show that Decision Tree performs slightly better than k-NN at classifying FTP and SSH attacks.

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“…The application of fractal analysis to detect attacks can be found in [18]. The authors in [19] dealt with a combination of fractal and recurrent functions. Different machine learning (ML) algorithms were used by [20].…”

Section: Related Workmentioning

confidence: 99%

One-Parameter Statistical Methods to Recognize DDoS Attacks

et al. 2022

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Within our academic high-speed network infrastructure which is used for connecting all universities and high schools in our country to the Internet, there are thousands of cybersecurity attacks occurring every day. That is why, within our SANET II project, an effort has been made to create a self-learning system without a teacher, which would be able to quickly adapt to arbitrary traffic and recognize DDoS attacks on time, even in high-speed networks, with a potential simple implementation into a hardware probe. In the article, we deal with the Hurst and autoregression coefficients and the coefficient of variation. We test the coefficients on simulated data and on real records of attacks. For early machine recognition of the attack, we propose the so-called predicting σ-tunnel. The obtained results can lead to the investigation of other prediction methods that would improve the early recognition of an attack.

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Classification Methods of Machine Learning to Detect DDoS Attacks

Cited by 30 publications

References 14 publications

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

A Review on DDoS Attacks Classifying and Detection by ML/DL Models

Comparing FTP and SSH Password Brute Force Attack Detection using k-Nearest Neighbour (k-NN) and Decision Tree in Cloud Computing

One-Parameter Statistical Methods to Recognize DDoS Attacks

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