[PDF] Top 20 An Intrusion Detection Model based on a Convolutional Neural Network
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An Intrusion Detection Model based on a Convolutional Neural Network
... is based on statistical and mathematical algorithms rather than rule-based ...of intrusion detection systems ...for intrusion detection and preprocess the KDD dataset through ... See full document
8
Object Detection in Underwater Images using Faster Region based Convolutional Neural Network
... consists of segmentation of detected object. The feature extraction process is carried out from the Gray Level Co- occurance Matrix (GLCM) technique. The classification process connected with optical information, like ... See full document
5
In-line recognition of agglomerated pharmaceutical pellets with density-based clustering and convolutional neural network
... region detection with DBSCAN (Fig. 3) separates the particles based on image gradients and intensities, which allows for easy classi- fication of the primary particles and the agglomerates based on ... See full document
6
Survey on Image Text Detection and Recognition of Natural Scene Images
... text detection by proposing a novel text convolutional neural network that particularly focuses on extracting text related regions and features from the image ...attentional ... See full document
5
Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network
... deep convolutional neural network (CNN) models have demon- strated extraordinary performance in medical image recognition tasks ...residual network [30]) and a cost-sensitive data-balancing ... See full document
20
Research on road extraction of remote sensing image based on convolutional neural network
... applied convolutional neural networks to target recognition inlarge scale natural ...R-CNN model obtained an average accuracy of about 20%higher than the traditional ...R-CNN model lays the ... See full document
11
An algorithm for highway vehicle detection based on convolutional neural network
... object detection has achieved significant advances in recent ...object detection frameworks can be di- vided into two categories: the two-stage approach, including [4–8], and one-stage approach, including ... See full document
7
Artificial Neural Network based Intrusion Detection System: A Survey
... artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input ...the network which remains underutilized or completely ...the ... See full document
6
Detection of medical text semantic similarity based on convolutional neural network
... CNN model. First, our CNN model is a supervised learning model and could automatically adapt feature representations to task ...CNN model could extract syntactic and semantic information from ... See full document
11
Intrusion Detection Systems: A Survey and Taxonomy
... Signature based detection systems, also referred to as a misuse detection; focuses on the network traffic and therefore attempts to catch any sequences or patterns of an inbound network ... See full document
6
Deep convolutional neural networks capabilities for
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. We.[r] ... See full document
26
Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. As a 21.[r] ... See full document
25
A General Study of Associations rule mining in Intrusion Detection System
... from network audit data as models of “normal ...rules based on Borgelt’s prefix trees, modifications to the computation of support and confidence of fuzzy rules, a new method for computing the similarity of ... See full document
10
Graph convolutional networks: a comprehensive review
... graph convolutional network-based deep learning model to use the information from multiple facts of the images from knowl- edge bases to aid question answering, which relies less on retrieving ... See full document
23
Entropy clustering based granular classifiers for network intrusion detection
... The KDDCUP99 data has 5,000,000 labeled records (viz. patterns) and 41 features (viz. input variables) pro- vided by the Massachusetts Institute of Technology. This dataset consists of 24 different types of attacks that ... See full document
10
Identification Of Weeds From Crops Using Convolutional Neural Network
... of network Architecture YannLeCun uses a new architecture which is good at object recognition in image dataset called the Convolutional Neural Network ...The convolutional technique is ... See full document
6
Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model
... the detection accuracy and precision of the PCA-PNN model are higher than that of the traditional PNN mod- el, and the false alarm rate is lower than that of PNN model when KDD99 data set is taken as ... See full document
8
NETWORK INTRUSION DETECTION USING DEEP NEURAL NETWORKS
... increasing. Intrusion detection system (IDS) is one of the important security issues ...A Network Intrusion Detection System (NIDS) helps system administrators to detect network ... See full document
9
Neural Network based Intrusion Detection Systems
... Also the proposed model was improved the detection rate for known and unknown attacks by training the hybrid model on the known intrusion data.. Then the model applied for unknown attack[r] ... See full document
6
A Detection algorithm based on Convolutional Neural Network
... the model is trained, 300 prior box will be calculated with the live ...the model, because we are ignorant of the particular case of the live box , using the regression frame to adjust the predicted prior ... See full document
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