DDoS Flooding Attack Mitigation in Software Defined Networks

Mahrach, Safaa; Haqiq, Abdelkrim

doi:10.14569/ijacsa.2020.0110185

Cited by 15 publications

(6 citation statements)

References 19 publications

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“…The controller manages network resources and communicates with all devices on the network, hence impacting all devices and services on the network when attacked. This enhances the effectiveness of the attack [ 9 ]. Overloading the controller can lead to difficulties in managing the system or even cause it to stop functioning [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

Oyucu,

Polat,

Türkoğlu

et al. 2023

Sensors

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Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

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Section: Introductionmentioning

confidence: 99%

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

Oyucu,

Polat,

Türkoğlu

et al. 2023

Sensors

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“…It is possible to reconfigure several devices simultaneously in SDN architecture. Configuring network devices is done at this tier using the application layer [9]. The SDN architecture's control layer (control plane) consists of a single controller.…”

Section: Introductionmentioning

confidence: 99%

DDoS attack detection in software defined networking controller using machine learning techniques

2022

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The term software defined networking (SDN) is a network model that contributes to redefining the network characteristics by making the components of this network programmable, monitoring the network faster and larger, operating with the networks from a central location, as well as the possibility of detecting fraudulent traffic and detecting special malfunctions in a simple and effective way. In addition, it is the land of many security threats that lead to the complete suspension of this network. To mitigate this attack this paper based on the use of machine learning techniques contribute to the rapid detection of these attacks and methods were evaluated detecting DDoS attacks and choosing the optimum accuracy for classifying these types within the SDN, the results showed that the proposed system provides the better results of accuracy to detect the DDos attack in SDN network as 99.90% accuracy of Decision Tree (DT) algorithm.

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“…RF has been found to show better outcomes with 97% accuracy. In addition, a practical and lightweight alleviation method has been introduced for protecting the SDN framework in contradiction to DDoS flooding, confirming an efficient and secure networking environment based on SDN [6]. The proposed system enhanced the DP with a mitigation and classification module to analyze all the new incoming packets and classify all the normal requests from SYN-flood attacks.…”

Section: Introductionmentioning

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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DDoS Flooding Attack Mitigation in Software Defined Networks

Cited by 15 publications

References 19 publications

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

DDoS attack detection in software defined networking controller using machine learning techniques

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

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