Efficient regional multi feature similarity measure based emotion detection system in web portal using artificial neural network

2020 ◽  
Vol 77 ◽  
pp. 103112 ◽  
Author(s):  
K. Dinakaran ◽  
EM. Ashokkrishna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Similarity Measure ◽  
Detection System ◽  
Emotion Detection ◽  
Web Portal ◽  
Feature Similarity ◽  
Artificial Neural

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.

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

2014 ◽  
Vol 3 (4) ◽  
pp. 156
Author(s):  
Gautam S. Prakash ◽  
Shanu Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Detection System ◽  
Structural Parameters ◽  
Signature Verification ◽  
Width Ratio ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Artificial Neural

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

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 A Study of Soft Computing Based IoT Device Security System

2020 ◽  
Vol 9 (11) ◽  
pp. 5-10
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security System ◽  
Security Risks ◽  
Iot Security ◽  
Security Attack ◽  
Artificial Neural ◽  
Iot Devices

The ubiquitous computing environment has increased interest in IoT technology. As IoT has open characteristics in the fields of industry, increased accessibility has raised the possibility of threats. As the IoT network was small on scale, there was risk of security. IoT development brought the network environment by combining networks, therefore risk of security attack compared to small network. The response time while operating IoT devices to detect intrusion through hacking, the artificial neural network responses using mobile devices. This process help to deal with hacking. By detecting virus in real time, this process help to prevent intrusion. As IoT security risks, we suggested an intrusion detection system using artificial neural network model in this study. The system which is developed in this can be adjusted to fit situations of IoT by facilitating modification of critical values. The research which detects anomaly through the response to be used for information security system which utilize IoT .

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