scholarly journals Multiple IoT based Network Attacks Discrimination by Multilayer Feedforward Neural Networks

Author(s):  
Vanya Ivanova

In this paper a new neural model for detection of multiple network IoT-based attacks, such as DDoS TCP, UDP, and HHTP flood, is presented. It consists of feedforward multilayer network with back propagation. A general algorithm for its optimization during training is proposed, leading to proper number of neurons in the hidden layers. The Scaled Gradient Descent algorithm and the Adam optimization are studied with better classification results, obtained by the developed classifiers, using the latter. Tangent hyperbolic function appears to be proper selection for the hidden neurons. Two sets of features, gathered from aggregated records of the network traffic, are tested, containing 8 and 10 components. While more accurate results are obtained for the 10-feature set, the 8-feature set offers twice lower training time and seems applicable for real-world applications. The detection rate for 7 of 10 different network attacks, primarily various types of floods, is higher than 90% and for 3 of them – mainly reconnaissance and keylogging activities with low intensity of the generated traffic, deviates between 57% and 68%. The classifier is considered applicable for industrial implementation.

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.


2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis


2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.


2011 ◽  
Vol 403-408 ◽  
pp. 3081-3085 ◽  
Author(s):  
Xin Ying Miao ◽  
Jin Kui Chu ◽  
Jing Qiao ◽  
Ling Han Zhang

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.


2011 ◽  
Vol 121-126 ◽  
pp. 4239-4243 ◽  
Author(s):  
Du Jou Huang ◽  
Yu Ju Chen ◽  
Huang Chu Huang ◽  
Yu An Lin ◽  
Rey Chue Hwang

The chromatic aberration estimations of touch panel (TP) film by using neural networks are presented in this paper. The neural networks with error back-propagation (BP) learning algorithm were used to catch the complex relationship between the chromatic aberration, i.e., L.A.B. values, and the relative parameters of TP decoration film. An artificial intelligent (AI) estimator based on neural model for the estimation of physical property of TP film is expected to be developed. From the simulation results shown, the estimations of chromatic aberration of TP film are very accurate. In other words, such an AI estimator is quite promising and potential in commercial using.


2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jiuwen Cao ◽  
Zhiping Lin

Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.


Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2761
Author(s):  
Vaios Ampelakiotis ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis ◽  
George Tsihrintzis

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.


Author(s):  
Dr. Gauri Ghule , Et. al.

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.


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