Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller

Mishra, Anupama; Gupta, Neena; Gupta, Brij B.

doi:10.1007/s11235-020-00747-w

Cited by 142 publications

(67 citation statements)

References 20 publications

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“…Several statistical approaches based on time series [ 21 ], multivariate linear regression [ 22 ], grey forecasting models [ 23 , 24 ], backpropagation neural networks [ 25 , 26 ] and simulation models [ 27 ] have been introduced by authors to predict ubiquitous cases. Many authors have applied models on different applications [ 28 – 30 ]. In another study, authors discussed different factors that affect epidemics and determined the dissemination of the disease [ 31 ], 32 .…”

Section: Related Workmentioning

confidence: 99%

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Raheja

Kasturia

Cheng

et al. 2021

Neural Comput & Applic

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The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Raheja

Kasturia

Cheng

et al. 2021

Neural Comput & Applic

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“…The development of several types of systems to ensure the security of critical infrastructures is the result of the need to deal with the threats and attacks in critical infrastructures such as nuclear power plants [13]. Scholars have proposed a number of technical measures that include technological tools to prevent [14,15], defend [16], detect [17], mitigate [18], and respond to cyber attacks. One way to check if a system is being attacked or an intruder has gained access to it is to detect abnormal behavior.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection in Critical Infrastructures: A Literature Review

et al. 2021

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Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions.

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“…Because the RTT increment is faster, the value of Cost gradually increases. 22), (24), and (25), versus RTT, meanwhile, presents the simulation results for comparison. We can observe that the simulation values are consistent with the theoretical values.…”

Section: Effect Of Network Parameters On Attack Performancementioning

confidence: 99%

“…Fig.22plots the curves of the theoretical TCP throughput for the D-and S-models according to (20)-(22),(24), and (25), versus RTT, meanwhile, presents the simulation results for comparison. We can observe that the simulation values are consistent with the theoretical values.…”

mentioning

confidence: 99%

High-Potency Models of LDoS Attack Against CUBIC + RED

Wu²,

Wang³

2021

IEEE Trans.Inform.Forensic Secur.

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A TCP-targeted low-rate denial of service (LDoS) attack exploits the vulnerabilities of TCP congestion control mechanism. The most widely used TCP congestion control algorithm, CUBIC, increases the resilience to LDoS. This paper explores high-potency patterns of LDoS attacks against CUBIC TCP under the RED queue management scenario, and develops two attack models, the D-and Smodels to maximize attack potency (i.e., the damage-to-cost ratio). Theoretical analyses and extensive experiments are conducted to validate the proper function of the models and evaluate their performance. Test results show that the models can effectively throttle CUBIC TCP throughput. Under standard-configured network parameters, one attack unit can damage up to about 21 and 26 TCP units for the D-and S-models, respectively, which represents an increase in attack potency about 20%. The attack potencies of our proposed models are at least 250% greater than that of the traditional attack model. In addition, with variations in different network parameters, these two models are still efficient and alternatively maximize the attack potency. Finally, attack countermeasures are outlined. The present study offers a basis to explore new attack manners which may be exploited by attackers and inspires researchers to develop new measurements against such attack.

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Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller

Cited by 142 publications

References 20 publications

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Intrusion Detection in Critical Infrastructures: A Literature Review

High-Potency Models of LDoS Attack Against CUBIC + RED

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