Convolutional Neural Networks for classifying skin lesions

Pai, K. Ananth; Giridharan, Anandi

doi:10.1109/tencon.2019.8929461

Cited by 37 publications

(19 citation statements)

References 8 publications

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“…This section briefly introduces the core concept of two DNN models, CNN and LSTM used in this study [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…If several filters are used, several feature maps are obtained, which compose the output of the convolutional layer. Since the full input uses the same filter at each time, through sharing the weight of characteristics in the filter which generates a feature map, the convolutional layer can extract the characteristics of input and can use fewer parameters to reduce the model’s complexity [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

“…Numerous artificial intelligence (AI) methodologies [11,12,[14][15][16][17][18][19][20][21][22][23][24][25] are introduced to defend against LFA without any manual intervention [26][27][28][29][30]. The basic idea of the current AI-based methodology is to screenshot the current network status as the input sample, then to apply convolution and pooling operations to extract features of the LFA, and finally to decide the possibility of the current network suffering LFA.…”

Section: Lfa and Countermeasuresmentioning

confidence: 99%

“…To overcome the changing attack characteristics of LFA, numerous artificial intelligence (AI) methodologies [ 11 , 12 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] are introduced to defend against LFA through end-to-end functionality (Input: network status; Output: defending action) without any manual intervention [ 26 , 27 , 28 , 29 , 30 ]. The AI-based methodologies, therefore, reduce the inefficient labor cost, subjective judgment, and self-learning regarding the changing attack characteristics of LFA in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

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A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network

Chen

Lai

Jan

et al. 2021

Sensors

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(1) Background: Link flooding attacks (LFA) are a spatiotemporal attack pattern of distributed denial-of-service (DDoS) that arranges bots to send low-speed traffic to backbone links and paralyze servers in the target area. (2) Problem: The traditional methods to defend against LFA are heuristic and cannot reflect the changing characteristics of LFA over time; the AI-based methods only detect the presence of LFA without considering the spatiotemporal series attack pattern and defense suggestion. (3) Methods: This study designs a deep ensemble learning model (Stacking-based integrated Convolutional neural network–Long short term memory model, SCL) to defend against LFA: (a) combining continuous network status as an input to represent “continuous/combination attacking action” and to help CNN operation to extract features of spatiotemporal attack pattern; (b) applying LSTM to periodically review the current evolved LFA patterns and drop the obsolete ones to ensure decision accuracy and confidence; (c) stacking System Detector and LFA Mitigator module instead of only one module to couple with LFA detection and mediation at the same time. (4) Results: The simulation results show that the accuracy rate of SCL successfully blocking LFA is 92.95%, which is 60.81% higher than the traditional method. (5) Outcomes: This study demonstrates the potential and suggested development trait of deep ensemble learning on network security.

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“…This section briefly introduces the core concept of two DNN models, CNN and LSTM used in this study [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Lfa and Countermeasuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network

Chen

Lai

Jan

et al. 2021

Sensors

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“…Convolutional Neural Network also known as ConvNet or CNN is one of class deep learning neural network classification method. It use the architectures of a Multi-Layer Perceptron (MLP) approach [13]. CNN use matrix of pixels from digital images as main object operations of the input data [14].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Tanned and Synthetic Leather Classification Based on Images Texture with Convolutional Neural Network

Faiz

Azhari

2020

Kno. Eng. Da. Sc.

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Tanned leather is an output from complex processes called tanning. Leather tanning is an important step that used to protect the fiber or protein structure of animal’s skin. Another reason of tanning process is to prevent the animal’s skin from any defect or rot. After the tanning is complete, the leather can be applied to produce a wide variety of leather products. Thus, the leather prices usually more expensive because it takes longer time in process. Another way to get cheaper price is make non-animal leather that usually known as synthetic or imitation leather. The purpose of this paper is to classify the tanned leather and synthetic leather by using Convolutional Neural Network. The tanned leather consist of cow, goat and sheep leathers. The proposed method will classify into four class, they are cow, goat, sheep and synthetic leathers. In each class consist of 160 images with 448x448 pixels size as the input data. With CNN method, this research shows a good result for the accuracy about 92.1%.

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