Convolutional Neural Network Based Algorithm for Early Warning Proactive System Security in Software Defined Networks

Janabi, Ahmed H.; Kanakis, Triantafyllos; Johnson, Mark

doi:10.1109/access.2022.3148134

Cited by 21 publications

(16 citation statements)

References 23 publications

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“…We compared the results of our research with the results of other studies that used a similar to our approach, with a mention of the type of algorithm used and the evaluation on which each study relied as we can see in table 10. LSTM, ACC=96.48% LSTM, ACC=98.04% [14] CNN, ACC=96.43% CNN, ACC=98.63% [10] GRU, Precision=89% GRU, Precision=97.3%…”

Section: Experiments Resultsmentioning

confidence: 99%

Untitled

2022

IJNSA

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Software-defined Networking (SDN) is a new technology for changing network architecture and making it more flexible and controllable. SDN can control all tasks of a network through the controller. Providing security for controller consider extremely important. Due side of the controller on the network side Network intrusion detection system (NIDS) will be effective to provide security for the controller. In this study, we suggest building a system (NIDS-DL) to detect attacks using 5 deep learning classifiers (DNN, CNN, RNN, LSTM, GRU). Our approach depends on the binary classification of the attacks. We used the NSL-KDD dataset in our study to train our deep learning classifiers. We employed 12 features extracted from 41 features using the feature selection method. CNN classifiers harvest the highest results in most evaluation metrics. Other classifiers also achieved good results. We compared our deep learning classifiers with each other and with other related studies. Our approach achieved success in identifying the attacks and might be used with great efficiency in the future.

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Section: Experiments Resultsmentioning

confidence: 99%

Untitled

2022

IJNSA

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“…When the deviation exceeds the preset threshold, the occurrence of fault can be inferred. On the other hand, since each measurement parameter is measured by each sensor installed in the unit, even if the sensor works under normal conditions, it will inevitably bring large or small measurement errors, which may make the value of some operation parameter exceed the reference range [13][14].…”

Section: Performance Monitoring and Fault Diagnosis Methods Of Gas Tu...mentioning

confidence: 99%

Dynamic Performance Analysis of Gas Turbine with Intermediate Cooling and Regenerative Based on Neural Network Algorithm

2022

KME

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Gas turbine is one of the most important large-scale equipment power plants at present, which has been widely used in the industrial field. Mechanical performance test is a very important parameter for gas turbine. Neural network algorithm can play a great role in system data processing and monitoring. Therefore, in order to observe the dynamic performance of gas turbine, neural network is used to study the data changes related to intermediate cooling and regeneration of gas turbine. This paper mainly uses the experimental method to compare, control the variable of pressure ratio, and analyze its dynamic performance through computer group efficiency, fuel consumption rate and the quality of carbon dioxide and oxygen. The experimental results show that, under rated load, the unit operates with variable pressure ratio. When the pressure ratio is less than 11, if efficiency is required, the unit power can be kept basically constant by changing the fuel quality.

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“…In this case, the counter of the flows will also be decreased and proceed to the following flow of the current request. The flow counter concept has already been published in [26].…”

Section: Decision Maker Modulementioning

confidence: 99%

Overhead Reduction Technique for Software-Defined Network Based Intrusion Detection Systems

2022

Self Cite

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In Software-Defined Networks, the Intrusion Detection System is receiving growing attention, due to the expansion of the internet and cloud storage. This system is vital for institutions that use cloud services and have many users. Although the Intrusion Detection System offers several security features, its performance is lagging behind in large enterprise's networks. Existing approaches are based on centralised processing and use many features to implement a protection system. Therefore, system overload and poor performance occur at the controller and OpenFlow switches. As a result, the current solutions create issues that must be considered, especially when they are implemented on large networks. Furthermore, enhancements in security applications improve the reliability of networks. Following a literature review of the existing Intrusion Detection Systems, this paper presents a new model that offers decentralised processing and exchanges data over a trusted, independent channel, in order to solve issues relating to system overload and poor performance. Our model utilises an appropriate feature selection method to reduce the number of extracted features and minimise the data transmitted over the channels. Additionally, the Naive Bayes algorithm has been employed for flow classification purposes, since it is a fast classifier. We successfully implemented our framework, using the Mininet emulator, which provides a suitable networking environment. Evaluations indicates that our proposed system can detect various attacks with an accuracy of 98.46% and nominal decreasing rates of 1.5% in throughput and 0.7% in latency analyses, when the model is implemented in wide range networks.

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Convolutional Neural Network Based Algorithm for Early Warning Proactive System Security in Software Defined Networks

Cited by 21 publications

References 23 publications

Untitled

Untitled

Dynamic Performance Analysis of Gas Turbine with Intermediate Cooling and Regenerative Based on Neural Network Algorithm

Overhead Reduction Technique for Software-Defined Network Based Intrusion Detection Systems

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