Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques

Wani, Abdul Raoof; Rana, Q. P.; Saxena, Utkarsh; Pandey, Nitin

doi:10.1109/aicai.2019.8701238

Cited by 87 publications

(35 citation statements)

References 6 publications

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“…However, the proposed approach must be tested with recent real-world datasets that contain new DDoS attack techniques. Zekri et al [64] proposed a DDoS detection system based on the C.4.5 algorithm. The algorithm was designed in such a way that it coupled with signature detection methods to achieve better DDoS classifications.…”

Section: A Ddos Defense Systems Based On ML Techniques In Cloud Compmentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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Section: A Ddos Defense Systems Based On ML Techniques In Cloud Compmentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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“…DDoS attacks are considered as sophisticated attacks and detection of such attacks have become a challenging task. The authors in [81] studied the a cloud environment using Tor Hammer as an attacking toop and then they created a dataset in order to detect the intrusions. They have utilized different ML algorithms including SVM, Navie Bayes, Random forest for classification and they demonstrated that SVM had the highest accuracy e.g., 99.7%.…”

Section: Svmmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

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“…Among the different types of datasets, the NSL-KDD dataset is selected due to its variety of features that could be suitable to evaluate the performance of our proposed model. The available NSL-KDD dataset in [19][20][21] was utilized to evaluate the ANN model in this study. The NSL-KDD Dataset is the update version of 99KDD dataset [22][23][24].…”

Section: Testing Datasetmentioning

confidence: 99%

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

Mohammed¹,

Arif²,

Ali³

2020

IJ-AI

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<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

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Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques

Cited by 87 publications

References 6 publications

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

A Review of Machine Learning Algorithms for Cloud Computing Security

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

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