Analysis of Features Dataset for DDoS Detection by using ASVM Method on Software Defined Networking

Oo, Myo Minn; Kamolphiwong, Sinchai; Kamolphiwong, Thossaporn; Vasupongayya, Sangsuree

doi:10.2991/ijndc.k.200325.001

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Ref. [85] proposed an Advanced-SVM algorithm for detecting UDP and SYN flood DDoS attacks in SDN networks. The proposed system was tested and trained using SDN-TrafficsDS and KDDCUP99 datasets, achieving overall average evaluation performance of 87%, 84%, and 93% for precision, recall, and F1-score, respectively.…”

Section: Single ML Approachesmentioning

confidence: 99%

“…In addition, some researchers run their approaches out of the SDN controller to reduce the load and overhead, mainly during DDoS attacks, such as [74,95]. In contrast, some studies did not provide details about where they deploy their approaches, such as [80,85,88]. Moreover, most approaches are designed to detect or mitigate DDoS attacks, and only a few can do both [83,92].…”

Section: Single ML Approachesmentioning

confidence: 99%

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A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

Bahashwan

Anbar

Manickam

et al. 2023

Sensors

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Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

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Section: Single ML Approachesmentioning

confidence: 99%

Section: Single ML Approachesmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

Bahashwan

Anbar

Manickam

et al. 2023

Sensors

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show abstract

“…The article [21] examined all the features extracted from SDN traffic, minimizing bias data from the dataset. The traffic features have been assessed through a tenfold-cross validation method.…”

Section: Review Of Existing Workmentioning

confidence: 99%

An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

J¹,

Sridaran²

2022

IJACSA

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Detecting Distributed Denial of Service (DDoS) attacks has become a significant security issue for various network technologies. This attack has to be detected to increase the system's reliability. Though various traditional studies are present, they suffer from data shift issues and accuracy. Hence, this study intends to detect DDoS attacks by classifying the normal and malicious traffic. The study aims to solve the data shift issues by using the introduced Decision Tree Detection (DTD) model encompassing of Greedy Feature Selection (GFS) algorithm and Decision Tree Algorithm (DTA). It also attempts to enhance the proposed model's detection rate (accuracy) above 90%. Various processes are involved in DDoS attack detection. Initially, the gureKddcup dataset is loaded to perform preprocessing. This process is essential for removing noisy data. After this, feature selection is performed to select only the relevant features, removing the irrelevant data. This is then fed into the train and test split. Following this, Software Defined Networking (SDN) based DTA is used to classify the normal and malicious traffic, then given to the trained model for predicting this attack. Performance analysis is undertaken by comparing the proposed model with existing systems in terms of accuracy, MCC (Matthew's Correlation Coefficient), sensitivity, specificity, error rate, FAR (False Alarm Rate), and AUC (Area under Curve). This analysis is carried out to evaluate the efficacy of the proposed model, which is verified through the results.

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Investigation of application layer DDoS attacks in legacy and software-defined networks: A comprehensive review

Kaur,

Sandhu,

Bhandari

2023

Int. J. Inf. Secur.

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Analysis of Features Dataset for DDoS Detection by using ASVM Method on Software Defined Networking

Cited by 5 publications

References 23 publications

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking

An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

Investigation of application layer DDoS attacks in legacy and software-defined networks: A comprehensive review

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