scholarly journals Efficient Detection of Phishing Websites Using Multilayer Perceptron

2020 ◽  
Vol 14 (11) ◽  
pp. 22
Author(s):  
Ammar Jamil Odeh ◽  
Ismail Keshta ◽  
Eman Abdelfattah
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Detection System ◽  
High Accuracy ◽  
Internet Fraud ◽  
Efficient Detection ◽  
Novel Approach

Phishing is a type of Internet fraud that aims to acquire the credential of users via scamming websites. In this paper, a novel approach is utilized that uses a Neural Network with a multilayer perceptron to detect the scam URL. The proposed system improves the accuracy of the scam detection system as it achieves a high accuracy percentage of 98.5%.

scholarly journals A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

2020 ◽  
Vol 9 (4) ◽  
pp. 609
Author(s):  
Abdulrahman Jassam Mohammed ◽  
Muhanad Hameed Arif ◽  
Ali Adil Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

scholarly journals Multilayer neural network synchronized secured session key based encryption in wireless communication

2019 ◽  
Vol 14 (1) ◽  
pp. 169 ◽  
Author(s):  
Arindam Sarkar ◽  
Joydeep Dey ◽  
Anirban Bhowmik
Keyword(s):  
Neural Network ◽  
Wireless Communication ◽  
Frequency Analysis ◽  
Multilayer Perceptron ◽  
Secret Key ◽  
Session Key ◽  
Novel Approach ◽  
Layered Networks ◽  
Hidden Layer ◽  
Energy Computation

<p>Energy computation concept of multilayer neural network synchronized on derived transmission key based encryption system has been proposed for wireless transactions. Multilayer perceptron transmitting machines accepted same input array, which in turn generate a resultant bit and the networks were trained accordingly to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights of hidden units of this selected hidden layer help to form a secret session key. A novel approach to generate a transmission key has been explained in this proposed methodology. The last thirty two bits of the session key were taken into consideration to construct the transmission key. Inverse operations were carried out by the destination perceptron to decipher the data. Floating frequency analysis of the proposed encrypted stream of bits has yielded better degree of security results. Energy computation of the processed nodes inside multi layered networks can be done using this proposed frame of work.</p>

scholarly journals DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK

2013 ◽  
Vol 6 (1) ◽  
pp. 127-136 ◽  
Author(s):  
Hamideh Kerdegari ◽  
Khairulmizam Samsudin ◽  
Abdul Rahman Ramli ◽  
Saeid Mokaram
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Fall Detection

scholarly journals A Novel Approach for Biofilm Detection Based on a Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 881 ◽  
Author(s):  
Giovanni Dimauro ◽  
Francesca Deperte ◽  
Rosalia Maglietta ◽  
Mario Bove ◽  
Fabio La Gioia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Accurate Identification ◽  
Protective Function ◽  
Bacterial Colony ◽  
Novel Approach ◽  
Nasal Cytology ◽  
Nasal Region ◽  
Validation Set

Rhinology studies anatomy, physiology and diseases affecting the nasal region: one of the most modern techniques to diagnose these diseases is nasal cytology or rhinocytology, which involves analyzing the cells contained in the nasal mucosa under a microscope and researching of other elements such as bacteria, to suspect a pathology. During the microscopic observation, bacteria can be detected in the form of biofilm, that is, a bacterial colony surrounded by an organic extracellular matrix, with a protective function, made of polysaccharides. In the field of nasal cytology, the presence of biofilm in microscopic samples denotes the presence of an infection. In this paper, we describe the design and testing of interesting diagnostic support, for the automatic detection of biofilm, based on a convolutional neural network (CNN). To demonstrate the reliability of the system, alternative solutions based on isolation forest and deep random forest techniques were also tested. Texture analysis is used, with Haralick feature extraction and dominant color. The CNN-based biofilm detection system shows an accuracy of about 98%, an average accuracy of about 100% on the test set and about 99% on the validation set. The CNN-based system designed in this study is confirmed as the most reliable among the best automatic image recognition technologies, in the specific context of this study. The developed system allows the specialist to obtain a rapid and accurate identification of the biofilm in the slide images.

scholarly journals Early Risk Detection of Pre-eclampsia for Pregnant Women Using Artificial Neural Network

2019 ◽  
Vol 15 (02) ◽  
pp. 71
Author(s):  
Endah Purwanti ◽  
Ichroom Septa Preswari ◽  
Ernawati Ernawati
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Maternal Mortality ◽  
Pregnant Women ◽  
Detection System ◽  
Maternal Mortality Ratio ◽  
High Accuracy ◽  
Mortality Ratio ◽  
Artificial Neural ◽  
Classification Tool

Pre-eclampsia still dominates maternal mortality cases in Indonesia. One effort that can be done is to establish early detection of the risk of pre-eclampsia in pregnant women. Automated devices with high accuracy are needed to detect the risk of pre-eclampsia so that the maternal mortality ratio can be reduced. This study aims to design an early detection system for the risk of pre-eclampsia based on artificial neural networks. The system is designed with 11 input parameters in the form of risk factors and output in the form of positive or negative risk of pre-eclampsia. The classification tool used in this study is backpropagation neural network with cross validation scenario at the training stage. The advantage of this system is the weighting of risk factor parameters by obstetric and gynecology specialists so that the results of testing the device show high accuracy. In addition, the device for early detection of pre-eclampsia was also conducted by user acceptance tests for a number of pregnant women.

Development of a Awaking Behavior Detection System Using a Neural Network

2008 ◽  
Vol 128 (11) ◽  
pp. 1649-1656 ◽  
Author(s):  
Hironobu Satoh ◽  
Fumiaki Takeda ◽  
Yuhki Shiraishi ◽  
Rie Ikeda
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Behavior Detection

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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