An experimental evaluation of the impact of the EDoS attacks against cloud computing services using AWS

Bulla, Suneetha; Rao, B. Basaveswara; Rao, K. Gangadhara; Chandan, K.

doi:10.14419/ijet.v7i1.5.9147

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…This type of attacks causes the interruption of cloud services to legitimate users by inordinate resource consumption which would automatically results in Service Level Agreement (SLA) violation. Most of the Cloud services are inherently elastic so the DDoS attacks are damaging the cloud service provider (CSP) economically but not its physical system or server assets [3]. This phenomenon is known as Economic Denial of Sustainability (EDoS) EDoS attack.…”

Section: Introductionmentioning

confidence: 99%

Investigative Study of the Effect of Various Activation Functions with Stacked Autoencoder for Dimension Reduction of NIDS using SVM

Narisetty¹,

Kancherla²,

Bobba³

et al. 2021

IJACSA

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Deep learning is one of the most remarkable artificial intelligence trends. It remains behind numerous recent achievements in various domains, such as speech processing, and computer vision, to mention a few. Likewise, these achievements have sparked great attention in utilizing deep learning for dimension reduction. It is known that the deep learning algorithms built on neural networks contain number of hidden layers, activation function and optimizer, which make the computation of deep neural network challenging and, sometimes, complex. The reason for this complexity is that obtaining an outstanding and consistent result from such deep architecture requires identifying number of hidden layers and suitable activation function for dimension reduction. To investigate the aforementioned issues linear and non-linear activation functions are chosen for dimension reduction using Stacked Autoencoder (SAE) when applied to Network Intrusion Detection Systems (NIDS). To conduct experiments for this study various activation functions like linear, Leaky ReLU, ELU, Tanh, sigmoid and softplus have been identified for the hidden and output layers. Adam optimizer and Mean Square Error loss functions are adopted for optimizing the learning process. The SVM-RBF classifier is applied to assess the classification accuracies of these activation functions by using CICIDS2017 dataset because it contains contemporary attacks on cloud environment. The performance metrics such as accuracy, precision, recall and Fmeasure are evaluated along with theses classification time is being considered as an important metric. Finally it is concluded that ELU is performed with low computational overhead with negligible difference of accuracy that is 97.33% when compared to other activation functions.

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Section: Introductionmentioning

confidence: 99%

Investigative Study of the Effect of Various Activation Functions with Stacked Autoencoder for Dimension Reduction of NIDS using SVM

Narisetty¹,

Kancherla²,

Bobba³

et al. 2021

IJACSA

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show abstract

“…The attack makes the cloud unsustainable via fading the cloud billing to impose the cloud user's bill intended for the attack's activities. This issue can be averted by limiting or controlling resource allocation (RA) (Ribin Jones and Kumar 2019; Bulla et al 2018). Nevertheless, some most imperative advantages of CC will be restricted.…”

Section: Introductionmentioning

confidence: 99%

An efficient EDoS-DOME system in cloud computing using obfuscated IP spoofing technique and RCDH-ENN detection technique

Jones

Kumar

2021

Appl Nanosci

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Cloud computing (CC) permits the end-users to access the network via a shared resources field. The vulnerabilities on the service providers will augment with the augmentation of the demand for CC. Economical denial of service (EDoS) attacks take over the supplier financially affecting the disparate organizations that utilize the cloud data. It is not possible to identify the hackers subsequent to EDoS attacks; however, their passage can well be detected and blocked. Nevertheless, loads of challenges are there to surmount. The work renders an effectual EDoS-Dome system in CC. Secured user authentication is initiated here with the instigation of the secret question key technique in the user registration together with the verification phase. After that, for the effective tracing back of the hacked data, an effectual Obfuscation technique is developed aimed at IP spoofing. To attain fast response time (RT) along with block the passage of attackers, a CI-RDA load balancer is developed. Lastly, the developed regression coefficients deer hunting-deep Elman neural network classifies the user data into a blacklist or white list centered on particular conditions. The experimentation's outcomes exhibit that the proposed work is effectual with 97.01% accuracy and 97.05% recalls when weighed against prevailing methods to classify the attacks. It also encompasses lower cost as well as fast RT with the equivalent web services, which signifies a safe model in opposition to the EDoS attack. KeywordsCloud computing • Distributed denial of service (DDoS) • Economical denial of service (EDoS) • EDoS Dome system • Internet protocol (IP) • IP spoofing • Confidence interval-red deer algorithm (CI-RDA) • And regression coefficients deer hunting-deep Elman neural network (RCDH-ENN)

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HRF (HTTP Request Filtering): A New Detection Mechanism of EDoS Attack on Cloud

Rao

Bulla

Rao

et al. 2019

2019 International Carnahan Conference on Security Technology (ICCST)

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An experimental evaluation of the impact of the EDoS attacks against cloud computing services using AWS

Cited by 5 publications

References 4 publications

Investigative Study of the Effect of Various Activation Functions with Stacked Autoencoder for Dimension Reduction of NIDS using SVM

Investigative Study of the Effect of Various Activation Functions with Stacked Autoencoder for Dimension Reduction of NIDS using SVM

An efficient EDoS-DOME system in cloud computing using obfuscated IP spoofing technique and RCDH-ENN detection technique

HRF (HTTP Request Filtering): A New Detection Mechanism of EDoS Attack on Cloud

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