Online DDoS attack detection using Mahalanobis distance and Kernel-based learning algorithm

Çakmakçı, Salva Daneshgadeh; Kemmerich, Thomas; Ahmed, Tarem; Baykal, Nazife

doi:10.1016/j.jnca.2020.102756

Cited by 37 publications

(3 citation statements)

References 32 publications

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“…However, the environmental variations that exist in this technique affect the detection performance. To improve the detection performance, Çakmakçı et al (2020) designed Mahalanobis distance and Kernel-based learning models for online DDoS attack identification. At first, entropy and statistical features were extracted for a better detection process.…”

Section: Motivationmentioning

confidence: 99%

An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

Boopathi

Chavan

J.³

et al. 2022

IJPCC

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Purpose The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method. Design/methodology/approach This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques. Findings The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively. Originality/value The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

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Section: Motivationmentioning

confidence: 99%

An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

Boopathi

Chavan

J.³

et al. 2022

IJPCC

View full text Add to dashboard Cite

show abstract

“…However, the dataset used in the proposed model is old, and the authors need to validate their approach in a large-scale real environment. Çakmakçı et al [128] proposed a novel DDoS detection method in which the proposed algorithm is improved based on a kernelbased online anomaly detection approach. The proposed work used an unsupervised machine learning algorithm and the CICIDS2017 dataset.…”

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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“…However, while bringing essential improvements to the current network architecture, the SDN, as any centralized service, has as a critical failure spot its central controller. Malicious users may target this controller aiming to impair the whole network operation through the usage of different approaches, such as intrusions (Lopez-Martin et al, 2017) and denial of service (DoS) attacks (Daneshgadeh C ¸akmakçı et al, 2020;Wang et al, 2020;Xu et al, 2020;Zhang et al, 2020). Thus, efficient protection mechanisms are needed in SDNs to guarantee the availability of the network and the quality of the provided services (Correa Chica et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A GRU deep learning system against attacks in software defined networks

Assis

Carvalho

Lloret

et al. 2021

Journal of Network and Computer Applications

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Online DDoS attack detection using Mahalanobis distance and Kernel-based learning algorithm

Cited by 37 publications

References 32 publications

An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

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

A GRU deep learning system against attacks in software defined networks

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