Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques

Sudar, K. Muthamil; Beulah, M.; Deepalakshmi, P.; Nagaraj, P.; Chinnasamy, P.

doi:10.1109/iccci50826.2021.9402517

Cited by 89 publications

(31 citation statements)

References 10 publications

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“…The information is secured from DDoS attack by using machine learnings algorithms to detect malicious network traffic. The KDD99 dataset is used by authors [14] to train and test the data using SVM and decision tree algorithm and it is found that SVM has higher precision rate [15]. It is found that the performance of IDS is increased by reduction of false alarm rate to provide the security to Unmanned Ariel Vehicles (UAV) network systems.…”

Section: Related Workmentioning

confidence: 99%

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Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Shyla¹,

Bhatnagar²

2023

Intelligent Automation &Amp; Soft Computing

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The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model's performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS (Intrusion Detection System) and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes, Decision Tree, K-Nearest Neighbors and Logistic Regression over KDD 99 dataset. The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy, macro average, and weighted average. The findings were concluded as a percentage increase in accuracy, precision, sensitivity, specificity, and a decrease in misclassification as 9.3%, 6.4%, 12.5%, 5.2% and 81%.

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Section: Related Workmentioning

confidence: 99%

“…The assumption of independent attributes as predictors makes prediction difficult with mutually independent data [14].…”

Section: Sdn Decision Tree and (Support Vector Machine) Svmmentioning

confidence: 99%

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Shyla¹,

Bhatnagar²

2023

Intelligent Automation &Amp; Soft Computing

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“…The aggregate value of these two distances results in a new feature value for training and test set which is utilized by the IDS for DDoS detection. Sudar et al (2021) presented a DDoS detection method utilizing machine learning algorithms along with SDN-based architecture. The proposed method employed highly accurate and significantly less complex machine learning algorithms, called Decision Tree (DT) and Support Vector Machine (SVM) for the classification of incoming data traffic into normal or attack.…”

Section: Ddos Detection Based On Machine Learningmentioning

confidence: 99%

Distributed Denial-of-Service (DDoS) Attacks and Defense Mechanisms in Various Web-Enabled Computing Platforms

Singh

Gupta

2022

International Journal on Semantic Web and Information Systems

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The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.

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“…It is quite useful for categorizing and continually examining data [21]. J48 algorithm has been used in identify of DDoS by [22]- [24].…”

Section: J48 or C45mentioning

confidence: 99%

Fast and accurate classifying model for denial-of-service attacks by using machine learning

Kareem

Jasim

2022

Bulletin EEI

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A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.

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Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques

Cited by 89 publications

References 10 publications

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Distributed Denial-of-Service (DDoS) Attacks and Defense Mechanisms in Various Web-Enabled Computing Platforms

Fast and accurate classifying model for denial-of-service attacks by using machine learning

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