Comprehensive Method of Botnet Detection Using Machine Learning

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Malicious Code ◽  
Large Network ◽  
Botnet Detection ◽  
Comprehensive Method ◽  
Input Layer ◽  
Optimal Accuracy ◽  
Artificial Neural ◽  
Hidden Layer

The botnet interrupts network devices and keeps control of the connections with the command, which controls the programmer, and the programmer controls the malicious code injected in the machine for obtaining information about the machines. The attacker uses a botnet to commence dangerous attacks as DDoS, phishing, despoil of information, and spamming. The botnet establishes with a large network and several hosts belong to it. In the paper, the authors proposed the framework of botnet detection by using an Artificial Neural Network. The author research upgrading the extant system by comprising of cache memory to fast the process. Finally, for detection, the author used an analytical approach, which is known as an artificial neural network that contains three layers: the input layer, hidden layer, output layer, and all layers are connected to correlate and approximate the results. The experiment result determines that the classifier with 25 epochs gives optimal accuracy is 99.78 percent and shows the detection rate is 99.7 percent.

scholarly journals The effect of Kurtosis on the accuracy of artificial neural network predictive model

2018 ◽  
Vol 204 ◽  
pp. 02018
Author(s):  
Aisyah Larasati ◽  
Anik Dwiastutik ◽  
Darin Ramadhanti ◽  
Aal Mahardika
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

scholarly journals Artificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging

2021 ◽  
Vol 21 (2) ◽  
pp. 241
Author(s):  
Joselito Abierta Olalo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Techniques ◽  
Fuel Oil ◽  
Oil Yield ◽  
Coconut Husk ◽  
Input Layer ◽  
Artificial Neural ◽  
Predicted Values ◽  
Hidden Layer

Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.

scholarly journals Optimasi Model Artificial Neural Network untuk Klasifikasi Paket Jaringan

SIMETRIS ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 1-5
Author(s):  
Indra Gunawan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Packet Size ◽  
Input Layer ◽  
Packet Length ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Game Browsing

Klasifikasi jaringan internet dibutuhkan secara luas oleh berbagai pihak untuk penghematan, pengalokasian, pembatasan sumber daya internet. Berbagai teknik digunakan untuk hal tersebut, salah satunya adalah dengan pendekatan machine learning khususnya algoritma jaringan syaraf tiruan (artificial neural network) yang selanjutnya disingkat ANN. ANN bekerja dengan cara meniru cara kerja syaraf otak manusia. AAN pada penelitian ini digunakan untuk mengklasifikasikan paket jaringan. Dataset yang digunakan adalah terdiri dari 990.558 baris data, 4 kolom input(X) meliputi protocol, port, timestamp, packet length. Kolom output(Y) terdiri satu kolom 5 label (App, Sosmed, Game, Browsing, Streaming). Selanjutnya dataset ini dibagi menjadi 3 yaitu training, test, dan validation. Tujuan dari penelitian ini adalah, pertama, untuk mengetahui kemampuan algoritma ANN untuk pengklasifikasian paket berdasarkan per satu paket. Kedua, menemukan model ANN yang paling optimal untuk permasalahan diatas. Kesimpulan yang didapatkan adalah Algoritma neural network dapat digunakan pada klasifikasi paket jaringan, tetapi jika dataset yang digunakan memiliki karakteristik-karakteristik seperti jumlah variabel X kecil, data pada variabel X sangat lebar jaraknya seperti (port, packet size, time to previous packet, protocol) maka akurasi tinggi sulit untuk didapatkan. Kedua, jika permasalahan yang diselesaikan memiliki kemiripan dengan penelitian ini, maka arsitektur model ANN yang paling optimal adalah: jumlah neuron input layer adalah 7 kali jumlah variabel X, jumlah neuron hidden layer adalah 1/2,8 kali jumlah neuron input, jumlah hidden layer satu, nilai dropout 0,33, metode aktivasi tanh-tanh-softmax, metode optimasi adamax. Nilai accuracy stabil didapatkan pada iterasi (epoch) ke-600, nilai loss stabil didapatkan pada epoch ke-1000. Nilai accuracy yang didapatkan sebesar 0,8 dan nilai loss 0,32 pada iterasi ke seribu.

scholarly journals Pengujian Algoritma Artificial Neural Network (ANN) Untuk Prediksi Kecepatan Angin

2019 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
Syukri Syukri ◽  
Samsuddin Samsuddin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

<span lang="EN-US">Angin memiliki peran yang penting dalam kehidupan manusia, antara lain pada pembangkit listrik, pelayaran dan penerbangan. Ketiga sektor tersebut erat kaitannya dengan  kondisi angin. Angin dapat muncul setiap saat dan setiap waktu serta perubahan geografis pada suatu wilayah. Hal ini mengakibatkan sulitnya menentukan kecepatan angin, maka untuk mengatasi masalah tersebut diperlukan prediksi kecepatan angin. Saat ini berbagai metode prediksi telah banyak dikembangkan, salah satu metode yang dapat digunakan untuk melakukan prediksi dengan akurasi yang tinggi yaitu algoritma <em>Artificial Neural Network</em> (ANN) <em>Backpropagation</em>. Arsitektur ANN yang digunakan adalah  4 parameter <em>input layer</em>, <em>hidden layer</em> (5, 10, 15, 20, 25 dan 30) dan <em>output layer</em> (1 parameter). Data pembelajaran dan pengujian didapatkan dari stasiun BMKG Blang Bintang Aceh Besar, berupa data kecepatan angin jam per harian periode Januari 2011 sampai dengan Desember 2015 yang terdiri dari arah angin, suhu, tekanan, kelembaban dan suhu. Hasil pengujian menunjukkan bahwa metode ANN <em>Backpropagation </em>cukup baik diterapkan untuk proses prediksi, kemampuan ANN dalam melakukan prediksi memiliki tingkat akurasi rata – rata yang lebih baik yaitu 96 %. Sedangkan nilai rata – rata kerapatan daya angin jam per harian yaitu </span><span lang="EN-US">45.030 W/m<sup>2</sup></span>

scholarly journals PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT

2018 ◽  
Vol 7 (1) ◽  
pp. 64-72
Author(s):  
Ekky Rosita Singgih Wigati ◽  
Budi Warsito ◽  
Rita Rahmawati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Processing System ◽  
Training Data ◽  
Neural Network Modeling ◽  
Feed Forward Neural Network ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Hidden Layer

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE 

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

Rapid Method for Prediction of Escherichia coli Numbers Using an Electronic Sensor Array and an Artificial Neural Network

2004 ◽  
Vol 67 (8) ◽  
pp. 1604-1609 ◽  
Author(s):  
UBONRATANA SIRIPATRAWAN ◽  
JOHN E. LINZ ◽  
BRUCE R. HARTE
Keyword(s):  
Neural Network ◽  
Escherichia Coli ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Sensor Array ◽  
E Coli ◽  
Output Layer ◽  
Electronic Sensor ◽  
Artificial Neural ◽  
Hidden Layer

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37°C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 105 CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography–mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.

Sign in / Sign up

Export Citation Format

Share Document