Short-term daily peak load forecasting using fast learning neural network

Khan, Gul Muhammad; Khan, Shariq Mahmood; Ullah, Fahad

doi:10.1109/isda.2011.6121762

Cited by 22 publications

(12 citation statements)

References 19 publications

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“…Various other types of ANNs have been evolved using CGP including convolutional ANNs [99] and wavelet ANNs [47,50]. There is now a growing literature on CGPANNs and they have been applied to many other applications including financial [130],medical [1][2][3][4], client prediction [5], load forecasting [41,42,45], internet traffic estimation [44] and signal reconstruction [51].…”

Section: Applicationsmentioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are wellknown to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.

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Section: Applicationsmentioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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“…Therefore, machine learning can be used for flow-based anomaly detection system to automatically build a predictive model based on the training dataset. To solve numerous classification and prediction problems machine learning algorithms are used [30]. A complete flow chart of the anomaly detection mechanism in OpenFlow controller is shown in Figure 2.…”

Section: Machine Learning Approachmentioning

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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“…Therefore, machine learning can be used for flow-based anomaly detection system to automatically build a predictive model based on the training dataset. To solve numerous classification and prediction problems machine learning algorithms have been used [25]. A complete flow chart of anomaly detection mechanism in OpenFlow controller is shown in Figure 2.…”

Section: Machine Learning Approachmentioning

confidence: 99%

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

Dey¹,

Rahman²

2019

Preprint

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Recent advancements in Software Defined Networking (SDN) makes it possible to overcome the management challenges of traditional network by logically centralizing control plane and decoupling it from forwarding plane. Through centralized controllers, SDN can prevent security breach, but it also brings in new threats and vulnerabilities. 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 paper, 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 Gain Ratio feature selection evaluator. In the later phase, the second approach is combined with Gated Recurrent Unit Long Short-Term Memory based intrusion detection model based on Deep Neural Network (DNN) where we applied an appropriate ANOVA F-Test and Recursive Feature Elimination feature selection method to improve the classifier performance and achieved 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.

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Short-term daily peak load forecasting using fast learning neural network

Cited by 22 publications

References 19 publications

Cartesian genetic programming: its status and future

Cartesian genetic programming: its status and future

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

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