Journal ArticleDOI
FLNL: Fuzzy entropy and lion neural learner for EDoS attack mitigation in cloud computing
Sukhada Bhingarkar,Deven Shah +1 more
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TLDR
F fuzzy entropy and lion neural learner (FLNL) is utilized for the classification of cloud users to mitigate EDoS attacks in the cloud and results finalize that the proposed FLNL is effective.Abstract:
Cloud computing is a technology that allows the end-users to access the network through a shared area of resources. As the demand for the cloud computing increases, vulnerabilities in the service p...read more
Citations
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Journal ArticleDOI
Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments
TL;DR: The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems and enabled the detection and effective mitigation of EDoS attacks.
Journal ArticleDOI
R-EDoS: Robust Economic Denial of Sustainability Detection in an SDN-Based Cloud Through Stochastic Recurrent Neural Network
Phuc Trinh Dinh,Minho Park +1 more
TL;DR: In this paper, the authors proposed an enhanced scheme to detect and mitigate EDoS attacks efficiently and reliably, which is composed of online and offline phases, implementing a gated recurrent unit, which not only can capture complex temporal dependence relations in the data, but also can reduce the vanishing gradient problems in time series.
Proceedings ArticleDOI
A Comparative Approach to Mitigate Economic Denial of Sustainability (EDoS) in a Cloud Environment
Swati Nautiyal,Shruti Wadhwa +1 more
TL;DR: This paper proposed a new approach that uses Artificial Neural Network along with Genetic Algorithm that that classify the cloud server consumer and may lessen the EDoS attacks in the cloud environment.
Proceedings ArticleDOI
HRF (HTTP Request Filtering): A New Detection Mechanism of EDoS Attack on Cloud
TL;DR: This paper studies the impact of EDoS attacks with proposed detection mechanism of Web Application Firewall using HTTP Request Filtering technique anduated analytical queuing model for the proposed defense mechanism and also explains the work flow.
Journal ArticleDOI
Two-Phase Deep Learning-Based EDoS Detection System
Chien-Nguyen Nhu,Minho Park +1 more
TL;DR: In this article, the authors proposed a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the model requires only a short sequence length of five of the input data, leading to degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system.
References
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Journal ArticleDOI
An efficient fuzzy classifier with feature selection based on fuzzy entropy
TL;DR: This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure and investigates the use of fuzzy entropy to select relevant features.
Journal ArticleDOI
Detection of known and unknown DDoS attacks using Artificial Neural Networks
TL;DR: An Artificial Neural Network (ANN) algorithm is chosen to detect DDoS attacks based on specific characteristic features (patterns) that separate DDoS attack traffic from genuine traffic.
Journal ArticleDOI
Cloud security defence to protect cloud computing against HTTP-DoS and XML-DoS attacks
TL;DR: This paper recreate some of the current attacks that attackers may initiate as HTTP and XML, and introduces the use of a back propagation neutral network, called Cloud Protector, which was trained to detect and filter such attack traffic.
Journal ArticleDOI
Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN
N. Pandeeswari,Ganesh Kumar +1 more
TL;DR: This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network to improve the accuracy of the detection system.
Journal ArticleDOI
HIDS: A host based intrusion detection system for cloud computing environment
TL;DR: The paper reports a host based intrusion detection model for Cloud computing environment along with its implementation and analysis, which provides security as a service (SecaaS) in the infrastructure layer of the Cloud environment.
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