Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran, Senthil; Ramalakshmi, R.; Hussain, Irshad; Kavin, Balasubramanian Prabhu; Alshamrani, Sultan S.; AlGhamdi, Ahmed Saeed; Alshehri, Abdullah

doi:10.3390/s22030709

Cited by 38 publications

(28 citation statements)

References 65 publications

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“…During the upsampling process, the network combines the image information with the feature maps from the sibling encoders through a jump connection. The feature map of the last layer of the decoder is processed by a convolutional kernel of size 1 × 1 so that the number of channels equals the number of categories [ 21 ].…”

Section: Analysis Of Convolutional Neural Network and 3d Magnetic Res...mentioning

confidence: 99%

Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance

Pan

Yang

2022

Applied Bionics and Biomechanics

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In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3D magnetic resonance and design corresponding models for practical applications. A series of medical image segmentation models based on convolutional neural networks is proposed. In this paper, firstly, a separated attention mechanism is introduced in the model, which divides the input data into multiple paths, applies self-attention weights to adjacent data paths, and finally fuses the weighted values to form the basic convolutional block. This structure has multiple parallel data paths, which increases the width of the network and therefore improves the feature extraction capability of the model. Then, this paper proposes a bidirectional feature pyramid for medical image segmentation task, which has top-down and bottom-up data paths, and, together with jump connections, can fully interact with feature maps at different scales. After that, a new activation function Mish is introduced, and its advantages over other activation functions are experimentally demonstrated. Finally, for the situation that medical image annotations are not easy to obtain, a semisupervised learning method is introduced in the model training process, and the effectiveness of this method is experimentally demonstrated. The joint network first denoises the input image, then super-resolution mapping is performed on the noise-removed feature map, and finally, the super-resolution 3D-MR image is obtained. We update the network by combining the denoising loss and super-resolution loss during the joint network training process. The experimental results show that the joint network with denoising first and then super-resolution outperforms the joint network with other task order and outperforms the method that performs the two tasks separately and the proposed method in this paper has the optimal performance.

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Section: Analysis Of Convolutional Neural Network and 3d Magnetic Res...mentioning

confidence: 99%

Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance

Pan

Yang

2022

Applied Bionics and Biomechanics

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“…Optimal water application or control was achieved using a convolution neural network (CNN) for a sugarcane crop. The proposed CNN provided better water control with high accuracy compared to other models [67][68][69][70]. Although several factors help in attaining irrigation optimization, evapotranspiration is the most preferred as it is derived using other key parameters.…”

Section: Artificial Neural Network and Machine Learning For Irrigationmentioning

confidence: 98%

“…Among the three models, ANFIS is a neural network model, SVM is a machine learning model, and GEP is an evolutionary computing technique. The SVM-based model performed better than the other two models, with sunshine hours, humidity, relative humidity, air temperature average, and wind speed as the input parameters for the model [70][71][72][73][74].…”

Section: Artificial Neural Network and Machine Learning For Irrigationmentioning

confidence: 99%

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

Veerachamy

Ramalakshmi

Kavin

et al. 2022

Water

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The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications.

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“…In Figure 10 a,b, we compare the Hungarian-based content delivery scheme with the random content delivery scheme [ 48 , 49 , 50 , 51 , 52 , 53 ] for minimizing the overall content delay and the power consumption of the system, respectively. We show that the Hungarian-based delivery scheme works much better as compared to random content delivery.…”

Section: Performance Evaluationmentioning

confidence: 99%

Proactive Caching in D2D Assisted Multitier Cellular Network

Ahmad

Hussain

et al. 2022

Sensors

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Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption.

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Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Cited by 38 publications

References 65 publications

Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance

Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

Proactive Caching in D2D Assisted Multitier Cellular Network

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