Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

Tonkal, Özgür; Polat, Hüseyin; Başaran, Erdal; Cömert, Zafer; Kocaoğlu, Ramazan

doi:10.3390/electronics10111227

Cited by 76 publications

(37 citation statements)

References 44 publications

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“…Processing time and detection accuracy were the performance metrics used to assess the proposed model's performance. From the outcomes, it has been clear that the proposed system accomplished high accuracy of 97% with reduced false alarms [26], [27]. Similarly, issues have been solved by introducing an effective system named Prodefense to detect and mitigate DDoS attacks.…”

Section: Review Of Existing Workmentioning

confidence: 90%

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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Section: Review Of Existing Workmentioning

confidence: 90%

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

J¹,

Sridaran²

2022

IJACSA

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“…Similarly, Raghu et al 42 used this method for the classification of EEG signals. The NCA method was also applied at the feature extraction and classification stages for the classification of patients with breast cancer 43 and the identification of cyber‐attacks 44,45 . Recently, NCA was also successfully subjected to the hepatocellular carcinoma disease 23 …”

Section: Methodsmentioning

confidence: 99%

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek

Turza

Pławiak

2022

Numer Methods Biomed Eng

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Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.

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“…Tonkal et al 31 detected DDoS attacks (TCP, UDP, ICMP) on the SDN dataset generated by Nisha Ahuja 32 using KNN, DT, ANN, and SVM classifiers and evaluated performance based on accuracy, sensitivity, specificity, precision, and F1 score. The results show that DT performs best among all.…”

Section: Related Workmentioning

confidence: 99%

ONOS Flood Defender: An Intelligent Approach to Mitigate DDoS Attack in SDN

Aslam

Srivastava

Gore

2022

Trans Emerging Tel Tech

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Software-Defined Networking (SDN) has made its place in the networks as new technology. SDN's programmable behavior enables it to change behavior on the fly, provides instructions for the task's automatic performance, dynamic scaling, and service integration. These advantages have made SDN necessary in networks. However, SDN suffers from the threat of DDoS attack. We have developed an approach to mitigate these threats by creating an ONOS Flood Defender Application (OFD App). This app effectively detects DDoS attack using supervised and ensemble machine learning techniques and mitigates them by tracebacking the attack traffic to its origin. Our results show that ensemble machine learning techniques perform better than single machine learning algorithm to detect DDoS attack. Random Forest classifier (RFC), an ensemble technique, performs best with the highest accuracy of 99.3% in detecting DDoS attack, followed by XGBoost classifier with an accuracy of 99%. The proposed framework of the OFD app is implemented in a Mininet emulator and ONOS SDN controller. The application performs effectively in terms of time, accuracy, and overhead of the system. Our app efficiently mitigates the attacks, thereby preventing a tremendous amount of damage to legitimate users.

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Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

Cited by 76 publications

References 44 publications

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

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

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

ONOS Flood Defender: An Intelligent Approach to Mitigate DDoS Attack in SDN

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