SEDoS-7: A proactive mitigation approach against EDoS attacks in cloud computing

2017 ◽  
Author(s):  
R. Gopeshwar Rao ◽  
Manisha J. Nene
Keyword(s):  
Cloud Computing ◽  
Edos Attacks

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

2017 ◽  
Vol 7 (1.5) ◽  
pp. 202 ◽  
Author(s):  
Suneetha Bulla ◽  
B. Basaveswara Rao ◽  
K. Gangadhara Rao ◽  
K. Chandan
Keyword(s):  
Cloud Computing ◽  
Cloud Platform ◽  
Test Bed ◽  
Computing Services ◽  
Trial Test ◽  
Edos Attacks ◽  
Cloud Computing Services ◽  
Single Category ◽  
The One ◽  
The Impact

Cloud computing is that the one among the quickest making and rising development in IT trade on pay-as – you-go premise. Flexibility is that the one among the properties of the cloud computing, it exhibits the response for DDoS ambush and created new quite strike significantly EDoS assault .This paper displays the impact of EDoS assaults on the cloud computing services, touching on single category of service. A check demonstrate was made public, performed associated contrasted and an expositive lining model. The trial test-bed was directed on Amazon internet Services cloud platform, it catches the cloud edges and incorporates range of execution measurements and value measurements, as an instance, range of running cases on the cloud, latency or latency , usage of distributed computing assets, throughput, and also the caused value as a result of the assault. The outcomes square measure introduced and conclusions square measure talked concerning.

scholarly journals A Hybrid Secure Storage Scheme to Avoid EDOS Attacks in Cloud Computing

2020 ◽  
Vol 8 (5) ◽  
pp. 1236-1242
Keyword(s):  
Cloud Computing ◽  
Problem Solving ◽  
Prior Knowledge ◽  
Private Information ◽  
Black Box ◽  
Cloud Environment ◽  
Secure Storage ◽  
Storage Scheme ◽  
Edos Attacks ◽  
Box Method

In advent of cloud environment, cloud operator is not a completely trusted to put on private information, because of lack of consumer to cloud control. To assurance privacy, documents sharer deploy their encipher documents. Encipher documents dispense to among consumers using CP-ABE scheme. But it is not completely safe in opposition to different assaults. The prior knowledge cannot offer any verification ability to cloud operator whether the user can decipher or not. Various invaders may obtain lot of document by initiate EDoS assaults. The consumer of cloud abides cost. To handle above issues, this article suggests a problem solving plan to safe encipher cloud repository from EDoS assaults and maintain supply utilization. It utilizes CP-ABE tactics in a black-box method furthermore accomplish impulsive entryway contract epithetical CP-ABE. We tend to present 2 mechanisms for various styles, observed via achievement and shield research.

FLNL: Fuzzy entropy and lion neural learner for EDoS attack mitigation in cloud computing

2018 ◽  
Vol 09 (06) ◽  
pp. 1850049 ◽  
Author(s):  
Sukhada Bhingarkar ◽  
Deven Shah
Keyword(s):  
Cloud Computing ◽  
Feature Selection ◽  
Fuzzy Entropy ◽  
Cloud Data ◽  
The Neural Network ◽  
Lion Algorithm ◽  
Edos Attacks ◽  
Log File ◽  
Attack Mitigation ◽  
Lm Algorithm

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 provision also increase. EDoS is one of the attacks that take over the provider, financially affecting the various organizations which use the cloud data. This paper utilizes fuzzy entropy and lion neural learner (FLNL) for the classification of cloud users to mitigate EDoS attacks in the cloud. This technique includes a training phase, which creates a log file using various parameters and then transforms the features into database considering certain key features. There are two important stages in this classification approach: feature selection and classification. Here, the fuzzy entropy function is utilized for feature selection which effectively selects useful features without information loss. The classification is performed using lion neural learner (LNL) which incorporates Lion algorithm (LA) into the neural network and uses Levenberg–Marquardt (LM) algorithm. The experimental results finalize that the proposed FLNL is effective with 89% precision, 78% recall, and 83.13% of f-measure compared with the existing Naïve Bayes (NB), Neural [Formula: see text] Propagation [Formula: see text], and Neural [Formula: see text]–Marquardt [Formula: see text].

Enhanced-Adaptive Pattern Attack Recognition Technique (E-APART) Against EDoS Attacks in Cloud Computing

2015 ◽  
Vol 17 (3) ◽  
pp. 41-55
Author(s):  
Rohit Thaper ◽  
Amandeep Verma
Keyword(s):  
Cloud Computing ◽  
Pattern Analysis ◽  
Denial Of Service ◽  
Cloud Services ◽  
Distributed Denial Of Service ◽  
Cloud Platform ◽  
Edos Attacks ◽  
The Cost ◽  
Adaptive Pattern ◽  
Recognition Technique

Cloud Computing is most widely used in current technology. It provides a higher availability of resources to greater number of end users. In the cloud era, security has develop a reformed source of worries. Distributed Denial of Service (DDoS) and Economical Denial of Sustainability (EDoS) are attacks that can affect the ‘pay-per-use' model. This model automatically scales the resources according to the demand of consumers. The functionality of this model is to mitigate the EDoS attack by some tactical attacker/s, group of attackers or zombie machine network (BOTNET) to minimize the availability of the target resources, which directly or indirectly reduces the profits and increase the cost for the cloud operators. This paper presents a model called Enhanced-APART which is step further of the authors' previous model (APART) that can be used to mitigate the EDoS attack from the cloud platform and shows the nature of the attack. Enhanced-APART model offers pre-shared security mechanism to ensure the access of legitimate users on the cloud services. It also performs pattern analysis in order to detect the EDoS caused by BOTNET mechanism and includes time-based and key-sharing post-setup authentication scheme to prevent the replication or replay attacks and thus results in mitigation of EDoS attack.

Efficiency Evaluation of HRF mechanism on EDoS attacks in Cloud Computing Services

2022 ◽  
Vol 40 (1/2/3) ◽  
pp. 1
Author(s):  
J. Sunny Deol . G. ◽  
Suneetha Bulla ◽  
Nageswara Rao Jarapala ◽  
Veeraiah Duggineni ◽  
Rajendra Kumar G
Keyword(s):  
Cloud Computing ◽  
Efficiency Evaluation ◽  
Computing Services ◽  
Edos Attacks ◽  
Cloud Computing Services
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