Hybrid Approach for detecting DDOS Attacks in Software Defined Networks

Kaur, Gaganjot; Gupta, Phalguni

doi:10.1109/ic3.2019.8844944

Cited by 21 publications

(7 citation statements)

References 13 publications

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“…In terms of results, the paper finds that the autoencoder-based approach outperforms the other unsupervised learning algorithms on all three datasets in terms of accuracy and F1score [23]. This suggests that autoencoder detection is a promising technique for cyberattack detection [24].…”

Section: Discussionmentioning

confidence: 77%

Cyberattack Detection Using Unsupervised Learning Techniques

Islam,

Rashid

2024

Preprint

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Cybersecurity threats are becoming increasingly sophisticated and challenging to detect, making it critical to develop effective and robust detection mechanisms. Unsupervised learning techniques offer a promising approach for cyberattack detection, particularly in scenarios where labeled data are scarce or unavailable. In this research, we propose a novel approach for cyberattack detection using adversarial autoencoders that combines the traditional autoencoder architecture with an additional adversarial component. The proposed approach involves data collection and preprocessing, training an adversarial autoencoder model using unsupervised learning with adversarial training, detecting anomalies, and evaluating the performance of the approach. The proposed approach offers several benefits, including the ability to capture complex relationships in the data and generate realistic reconstructions of normal data. The effectiveness and potential of the proposed approach will be evaluated through extensive experiments and comparisons with existing unsupervised learning techniques or other benchmark methods.

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Section: Discussionmentioning

confidence: 77%

Cyberattack Detection Using Unsupervised Learning Techniques

Islam,

Rashid

2024

Preprint

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“…In terms of results, the paper nds that the autoencoder-based approach outperforms the other unsupervised learning algorithms on all three datasets in terms of accuracy and F1-score [23]. This suggests that autoencoders are a promising technique for cyberattack detection [24].…”

Section: Roc Curvementioning

confidence: 80%

Cyberattack Detection Using Unsupervised Learning Techniques

Islam,

Rashid

2024

Preprint

View full text Add to dashboard Cite

Cybersecurity threats are becoming increasingly sophisticated and challenging to detect, making it critical to develop effective and robust detection mechanisms. Unsupervised learning techniques offer a promising approach for cyberattack detection, particularly in scenarios where labeled data is scarce or not available. In this research, we propose a novel approach for cyberattack detection using adversarial autoencoders, which combine the traditional autoencoder architecture with an additional adversarial component. The proposed approach involves data collection and preprocessing, training an adversarial autoencoder model using unsupervised learning with adversarial training, detecting anomalies, and evaluating the performance of the approach. The proposed approach offers several benefits, including the ability to capture complex relationships in the data and generate realistic reconstructions of normal data. The effectiveness and potential of the proposed approach will be evaluated through extensive experiments and comparisons with existing unsupervised learning techniques or other benchmark methods.

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“…Although achieving a 95% accuracy with a low false alarm rate, the limited dataset may impact generalization. [12] aimed to improve accuracy using KNN and SVM models but only utilized two features, potentially limiting generalization. Similarly, [21] proposed a KPCA-GA-SVM algorithm, demonstrating a high accuracy of 98.09%.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Machine Learning Approaches for Classification of Ddos Attacks in Software Defined

Wabi,

Idris,

Olaniyi

et al. 2024

Preprint

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Enhancing network management and centralized network control is the goal of software-defined network. It separates the data plane from the control plane. Pros of SDN include its programmability, flexibility, and manageability to accommodate network users' ever-increasing demands. SDN is known to be a strong and safe defense against malicious network activity, but it is also susceptible to attacks like distributed denial-of-service attacks. This is a result of its centralized control features. To defend against this threat, a number of machine learning strategies have been employed. This study evaluated the performance of five Machine learning (ML) Algorithm (Naive Bayes, K-nearest neighbour, Random Forest, support vector machine, Logistic regression) for classifcation of traffic dataset as either normal or DDOS attacks in SDN. The study adopted 10-fold cross validation on each of the ML Algorithms and the result showed Random forest has the highest performance compared with other machine learning model while Naive Bayes had the lowest training time.

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Hybrid Approach for detecting DDOS Attacks in Software Defined Networks

Cited by 21 publications

References 13 publications

Cyberattack Detection Using Unsupervised Learning Techniques

Cyberattack Detection Using Unsupervised Learning Techniques

Cyberattack Detection Using Unsupervised Learning Techniques

Performance Evaluation of Machine Learning Approaches for Classification of Ddos Attacks in Software Defined

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