Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Livieris, Ioannis E.

doi:10.3390/a12040085

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…Recurrent neural network (RNN) [36] is a type of artificial neural network which has the capability of learning from previous time-steps. RNNs are extended forms of typical feed-forward neural networks (FNNs) [37]. But, in contrast with FNNs, RNNs use their internal state while processing sequential data.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Dey

Rahman²

2019

Symmetry

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Recent advancements in software-defined networking (SDN) make it possible to overcome the management challenges of traditional networks by logically centralizing the control plane and decoupling it from the forwarding plane. Through a symmetric and centralized controller, SDN can prevent security breaches, but it can also bring in new threats and vulnerabilities. The central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this research, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with random forest classifier using the gain ratio feature selection evaluator. In the later phase, the second approach is combined with a deep neural network (DNN)-based intrusion detection system based on gated recurrent unit-long short-term memory (GRU-LSTM) where we used a suitable ANOVA F-Test and recursive feature elimination selection method to boost classifier output and achieve an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.Symmetry 2020, 12, 7 2 of 21 of the network, taking advantage of decision-making from the global perspective of the network in the controller. However, to be successful in current dynamic environments, traffic monitoring becomes a cornerstone in SDN given that management applications often need to make use of accurate and timely traffic measurements. Forwarding components like switches, routers, etc., are elements of the data plane and the controller is the only element of the control plane. Decoupling of the network control and forwarding functions, and direct programmability of the network give network managers enough control over the network. Separation of the routing and forwarding activities of networking components (e.g., switches, routers, etc.) from the data plane, makes the administration and management of the network straightforward because the control plane now only has to handle logical network topology-related information, traffic routing, and so forth while the data plane only needs to manage the network traffic using the configuration provided by the control unit. The SDN paradigm was the outcome of some works of the research world that suggested the necessity of building programmable networks as a practical way to experiment with new protocols in production networks. From its inception, it has gained lots of attention from academia and industry. It is supported by giants of the Internet world like Google, Cisco, HP, Juniper or NEC and by standardization organizations like the Open Network Foundation (ONF) or the Internet Engineering Task Force (IETF), so one can state that this network paradigm has a lot of potential to succeed. The proposal of the OpenFlow protocol [2] in 2008 wa...

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Dey

Rahman²

2019

Symmetry

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“…In this learning method, the weights change their values according to the learning records until one epoch (an entire learning dataset) is reached. This method aims to minimize the error function, described as follows [14,78,79]: (11) where t = 1, . .…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece

et al. 2019

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This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.

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“…Taking into account the above consideration, we recall that WCNNs eliminate the likelihood that the weights can "blow up" to unrealistically large values by placing box-constraints on the weights. Furthermore, these new prediction models have been empirically proven by numerical experiments in a variety of real-world problems [19][20][21] to present better generalization ability compared to classical ANNs.…”

Section: Weight-constrained Neural Networkmentioning

confidence: 99%

“…We compared the prediction performance of WCNNs against classical ANNs. For this purpose, we utilized the performance profiles proposed by Dolan and Morè [26] to present perhaps the most complete information in terms of solution quality and efficiency [19,20]. Notice that the performance profile plots the fraction P of simulations for which any given model is within a factor τ of the best forecasting model.…”

Section: Performance Evaluation Of Wcnns Against Annsmentioning

confidence: 99%

Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study

et al. 2019

Self Cite

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Tourism forecasting is a significant tool/attribute in tourist industry in order to provide for careful planning and management of tourism resources. Although accurate tourist volume prediction is a very challenging task, reliable and precise predictions offer the opportunity of gaining major profits. Thus, the development and implementation of more sophisticated and advanced machine learning algorithms can be beneficial for the tourism forecasting industry. In this work, we explore the prediction performance of Weight Constrained Neural Networks (WCNNs) for forecasting tourist arrivals in Greece. WCNNs constitute a new machine learning prediction model that is characterized by the application of box-constraints on the weights of the network. Our experimental results indicate that WCNNs outperform classical neural networks and the state-of-the-art regression models: support vector regression, k-nearest neighbor regression, radial basis function neural network, M5 decision tree and Gaussian processes.

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Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Cited by 13 publications

References 28 publications

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking

Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece

Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study

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