Effective mitigation against IoTs using super materials for distributed denial of service attacks in cloud computing

Bhardwaj, Aanshi; Mangat, Veenu; Vig, Renu

doi:10.1016/j.matpr.2020.04.800

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…This computed value lies in the range between −1 and 1. Further, the two coefficient vectors can be expressed using Equation (10) and Equation (11).…”

Section: Position Updating By Leader Huntermentioning

confidence: 99%

“…But in recent years, the attack intrusion is increasing in the cloud network. Thus, it faces the challenges of detecting the attacks since it targets the cloud servers 11 …”

Section: Introductionmentioning

confidence: 99%

“…Thus, it faces the challenges of detecting the attacks since it targets the cloud servers. 11 Deep learning becomes an adverse technique that is developed by such scholars to enhance the attack mitigation performance in the cloud environment. It can perform an in-depth manner of neurological behavior of the human brain.…”

Section: Introductionmentioning

confidence: 99%

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Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

Rubai¹

2023

Trans Emerging Tel Tech

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Cloud computing is a paradigm that acts as an emerging technology with the intent of multimedia. Nevertheless, multimedia computing faces a financial burden. The essential characteristic of cloud computing is the cloud's pay‐as‐you‐go pricing model. Hence, several attacks are affecting cloud services since the threats are present long term. Such harmful attacks are named Fraudulent Resource Consumption (FRC) attacks. Due to this nature, attack detection is a critical task in cloud computing. To alleviate the problem, the proposed work is intended to detect and mitigate the FRC attack in the cloud without any vital issues. Initially, the HTTP web‐server logs are collected from the benchmark source, and the data preprocessing is performed for sequence generation, followed by the sequence decomposition process. Here, the Heuristic‐based Discrete Wavelet Transform is developed by the Adaptive Deer Hunting Optimization Algorithm (ADHOA) that is to be adopted for decomposing the sequence. Further, the hyper‐parameter Tuned‐Recurrent Neural Network (HT‐RNN) with estimating attack percentage is conducted for FRC detection. Once the detection of the FRC attack is done, then the mitigation procedure is processed for blocking them. Throughout the experimental analysis, the accuracy of the proposed ADHOA‐HT‐RNN method has attained 96%, and also, the precision of the proposed work offers 94%. The specificity of the designed model has secured 92%. The performance is assessed, and its result proves that the recommended system exploits the better detection performance. Thus, the simulation outcome has shown that the proposed ADHOA‐HT‐RNN model has attained superior performance compared to the other conventional approaches.

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“…This computed value lies in the range between −1 and 1. Further, the two coefficient vectors can be expressed using Equation (10) and Equation (11).…”

Section: Position Updating By Leader Huntermentioning

confidence: 99%

“…But in recent years, the attack intrusion is increasing in the cloud network. Thus, it faces the challenges of detecting the attacks since it targets the cloud servers 11 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

Rubai¹

2023

Trans Emerging Tel Tech

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“…Distributed denial-of-service (DDoS) attack is a very dangerous type of attack because it causes enormous losses to the server that is the victim of the attack [1][2]. Since it first appeared, until today's modern era, the types and methods of DDoS attacks have developed very rapidly [3].…”

Section: Introductionmentioning

confidence: 99%

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Sanmorino

Gustriansyah

Alie

2022

JUITA

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In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.

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Transfer Learning with CNN-LSTM for detection of Cardiac Events in Arrhythmia Disease

Arora,

Kaur,

Bhardwaj

2023

2023 Seventh International Conference on Image Information Processing (ICIIP)

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Effective mitigation against IoTs using super materials for distributed denial of service attacks in cloud computing

Cited by 4 publications

References 11 publications

Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

Development of hyper‐parameter‐tuned‐recurrent neural network for detection and mitigation of fraudulent resource consumption attack in cloud

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine

Transfer Learning with CNN-LSTM for detection of Cardiac Events in Arrhythmia Disease

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