scholarly journals NEURAL NETWORK APPROACHES FOR INTRUSION DETECTION AND RECOGNITION

2014 ◽  
pp. 118-125
Author(s):  
Vladimir Golovko ◽  
Leanid Vaitsekhovich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intrusion Detection ◽  
Data Extraction ◽  
Principal Component ◽  
Computational Time ◽  
Multilayer Perceptrons ◽  
Neural Network Models ◽  
Time Operation ◽  
Detection And Recognition

Most current Intrusion Detection Systems (IDS) examine all data features to detect intrusion. Also existing intrusion detection approaches have some limitations, namely impossibility to process large number of audit data for real-time operation, low detection and recognition accuracy. To overcome these limitations, we apply modular neural network models to detect and recognize attacks in computer networks. It is based on combination of principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). PCA networks are employed for important data extraction and to reduce high dimensional data vectors. We present two PCA neural networks for feature extraction: linear PCA (LPCA) and nonlinear PCA (NPCA). MLP is employed to detect and recognize attacks using feature-extracted data instead of original data. The proposed approaches are tested using KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.

scholarly journals Detailed Analysis of Top 10 AI- Deep Learning Neural Networks in Intrusion Detection for Internet of Things

2021 ◽  
Author(s):  
Kanimozhi V ◽  
T. Prem Jacob
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Models ◽  
Learning Models ◽  
Neural Network Models

Abstract Although there exist various strategies for IoT Intrusion Detection, this research article sheds light on the aspect of how the application of top 10 Artificial Intelligence - Deep Learning Models can be useful for both supervised and unsupervised learning related to the IoT network traffic data. It pictures the detailed comparative analysis for IoT Anomaly Detection on sensible IoT gadgets that are instrumental in detecting IoT anomalies by the usage of the latest dataset IoT-23. Many strategies are being developed for securing the IoT networks, but still, development can be mandated. IoT security can be improved by the usage of various deep learning methods. This exploration has examined the top 10 deep-learning techniques, as the realistic IoT-23 dataset for improving the security execution of IoT network traffic. We built up various neural network models for identifying 5 kinds of IoT attack classes such as Mirai, Denial of Service (DoS), Scan, Man in the Middle attack (MITM-ARP), and Normal records. These attacks can be detected by using a "softmax" function of multiclass classification in deep-learning neural network models. This research was implemented in the Anaconda3 environment with different packages such as Pandas, NumPy, Scipy, Scikit-learn, TensorFlow 2.2, Matplotlib, and Seaborn. The utilization of AI-deep learning models embraced various domains like healthcare, banking and finance, findings and scientific researches, and the business organizations along with the concepts like the Internet of Things. We found that the top 10 deep-learning models are capable of increasing the accuracy; minimize the loss functions and the execution time for building that specific model. It contributes a major significance to IoT anomaly detection by using emerging technologies Artificial Intelligence and Deep Learning Neural Networks. Hence the alleviation of assaults that happen on an IoT organization will be effective. Among the top 10 neural networks, Convolutional neural networks, Multilayer perceptron, and Generative Adversarial Networks (GANs) output the highest accuracy scores of 0.996317, 0.996157, and 0.995829 with minimized loss function and less time pertain to the execution. This article added to completely grasp the quirks of irregularity identification of IoT anomalies. Henceforth, this research analysis depicts the implementations of the Top 10 AI-deep learning models, which come in handy that assist you to perceive different neural network models and IoT anomaly detection better.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

2018 ◽  
Vol 6 (11) ◽  
pp. 216-216 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Marcus W. Beck ◽  
David A. Winkler ◽  
Bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Black Box ◽  
Clinical Applications ◽  
Neural Network Models

scholarly journals Robotic grasp detection using a novel two-stage approach

2021 ◽  
Vol 1 (1) ◽  
pp. 19-29
Author(s):  
Zhe Chu ◽  
Mengkai Hu ◽  
Xiangyu Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Particle Swarm Optimizer ◽  
Neural Network Models ◽  
Two Stage ◽  
The Neural Network ◽  
End To End ◽  
Small Change ◽  
Robotic Grasp

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

scholarly journals Music generation and human voice conversion based on LSTM

2021 ◽  
Vol 336 ◽  
pp. 06015
Author(s):  
Guangwei Li ◽  
Shuxue Ding ◽  
Yujie Li ◽  
Kangkang Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Short Term Memory ◽  
Human Life ◽  
Piano Music ◽  
Training Model ◽  
Voice Conversion ◽  
Neural Network Models ◽  
Human Voice ◽  
Music Generation

Music is closely related to human life and is an important way for people to express their feelings in life. Deep neural networks have played a significant role in the field of music processing. There are many different neural network models to implement deep learning for audio processing. For general neural networks, there are problems such as complex operation and slow computing speed. In this paper, we introduce Long Short-Term Memory (LSTM), which is a circulating neural network, to realize end-to-end training. The network structure is simple and can generate better audio sequences after the training model. After music generation, human voice conversion is important for music understanding and inserting lyrics to pure music. We propose the audio segmentation technology for segmenting the fixed length of the human voice. Different notes are classified through piano music without considering the scale and are correlated with the different human voices we get. Finally, through the transformation, we can express the generated piano music through the output of the human voice. Experimental results demonstrate that the proposed scheme can successfully obtain a human voice from pure piano Music generated by LSTM.

scholarly journals Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

2006 ◽  
Vol 71 (11) ◽  
pp. 1207-1218
Author(s):  
Dondeti Satyanarayana ◽  
Kamarajan Kannan ◽  
Rajappan Manavalan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mefenamic Acid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Uv Spectrophotometry ◽  
Novel Approach ◽  
Marquardt Algorithm ◽  
Calibration Models

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

2014 ◽  
Vol 651-653 ◽  
pp. 1772-1775
Author(s):  
Wei Gong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Data Fusion ◽  
Input Data ◽  
The Other ◽  
Pruning Algorithm ◽  
Detection Model ◽  
Process Ability

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

Artificial Polynomial and Trigonometric Higher Order Neural Network Group Models

2013 ◽  
pp. 78-102 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Continuous Function ◽  
Trigonometric Polynomial ◽  
Network Models ◽  
Higher Order ◽  
Financial Data ◽  
Neural Network Models ◽  
Piecewise Continuous ◽  
Discontinuous Data

Real world financial data is often discontinuous and non-smooth. Accuracy will be a problem, if we attempt to use neural networks to simulate such functions. Neural network group models can perform this function with more accuracy. Both Polynomial Higher Order Neural Network Group (PHONNG) and Trigonometric polynomial Higher Order Neural Network Group (THONNG) models are studied in this chapter. These PHONNG and THONNG models are open box, convergent models capable of approximating any kind of piecewise continuous function to any degree of accuracy. Moreover, they are capable of handling higher frequency, higher order nonlinear, and discontinuous data. Results obtained using Polynomial Higher Order Neural Network Group and Trigonometric polynomial Higher Order Neural Network Group financial simulators are presented, which confirm that PHONNG and THONNG group models converge without difficulty, and are considerably more accurate (0.7542% - 1.0715%) than neural network models such as using Polynomial Higher Order Neural Network (PHONN) and Trigonometric polynomial Higher Order Neural Network (THONN) models.

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