Entropy-Based Economic Denial of Sustainability Detection

Monge, Marco Antonio Sotelo; Vidal, Jorge Maestre; Villalba, Luis Javier García

doi:10.3390/e19120649

Cited by 16 publications

(4 citation statements)

References 41 publications

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“…It was demonstrated that there is a close link between the use of a cyclical economy and economic growth [51][52][53]. Other authors [54][55][56][57] concluded that human capital and innovation for environmental benefits have a positive impact on economic growth. Now, we will use entropy measures to evaluate the circular economy output for the 28 EU countries.…”

Section: Resultsmentioning

confidence: 99%

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Buṣu

Bușu

2018

Processes

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In this paper we propose a methodology to study circular economy processes based on mathematical modelling. In open-ended systems, waste could be converted back to recycling, transforming the economy from linear to circular. The concept of entropy and the second law of thermodynamics give the argument for a scale reduction of material circulation. As humans extract more and more energy and matter for the economy, the degree of entropy is likely to increase. Based on the findings of economic studies on the implications of industrialization in the case of growing economies, this study aims at evaluating circular economy processes at the European Union (EU) level using a Shannon-Entropy-based algorithm. An entropy-based analysis was conducted for the 28 European Union countries during the time frame 2007–2016. The modelling process consists of constructing a composite indicator which is composed of a weighted sum of all indicators developed by an algorithm based on Shannon Entropy. The weights assigned to each indicator in our analysis measure the significance of each indicator involved in the development of the composite indicator. The results are similar to the international rakings, consolidating and confirming the accuracy and reliability of this approach.

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Section: Resultsmentioning

confidence: 99%

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Buṣu

Bușu

2018

Processes

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“…EDoS attacks may be detected using statistical approaches, such as entropy and fuzzy methods [27]. The detection accuracy is excellent [28].…”

Section: Study Backgroundmentioning

confidence: 99%

Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments

Aldhyani

Alkahtani

2022

Sensors

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Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks.

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“…When zombie machines transmit a big quantity of unintended traffic in the direction of the cloud, exploiting the cloud's scalability in chalking up an exorbitant quantity of price on a cloud adopter's receipt, an EDoS attack befalls (Agrawal and Tapaswi 2020;Monge 2017). The attack makes the cloud unsustainable via fading the cloud billing to impose the cloud user's bill intended for the attack's activities.…”

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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Entropy-Based Economic Denial of Sustainability Detection

Cited by 16 publications

References 41 publications

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments

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

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