Android Malware Detection Using Backpropagation Neural Network

Huda, Fais Al; Mahmudy, Wayan Firdaus; Tolle, Herman

doi:10.11591/ijeecs.v4.i1.pp240-244

Cited by 6 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After partial derivative for each weight (biases included), the weight will then be updated. The weight update will follow the equation written in (8). The new weight for the next iteration ( ( )) will be the current weight ( ) reduced by the product of learning rate (η) and the partial derivative of the error ( ).…”

Section: A Neural Network and Backpropagationmentioning

confidence: 99%

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Priambodo¹,

Mahmudy²,

Rahman³

2020

KEDS

View full text Add to dashboard Cite

Even though it is inevitable, but it can be anticipated to minimize damage and casualties. Past research has been conducted to predict the level of impact caused by earthquakes in real-time [2]. One of those past research is about an early earthquake warning system (EEWS), which will give an alert when it detects an earthquake [3]. Numerous architectures and algorithms have been developed in those studies. Various research is the utilization of neural network trained with backpropagation algorithm and optimized using Levenberg (LOM) to predict hypocenter location, moment magnitude, and the expansion of the earthquake [4], modification of LOM to minimize error on EEWS [3], and utilization of neural tree to predict P and S waves [5]. Until now, to the knowledge of the author, the study of using a backpropagation (BP) algorithm to predict earthquake magnitude and grid-based location in Indonesia has not been conducted yet. This algorithm is chosen because it has been proven to perform well in broad types of problems, such as regression, pattern recognition, and prediction [6][7][8][9]. In this paper, the study aims to measure the performance of neural network trained using backpropagation algorithm in predicting earthquakes magnitude and grid-based location based on earthquakes magnitude and location data in Indonesia recorded from 2000 to 2019.

show abstract

Section: A Neural Network and Backpropagationmentioning

confidence: 99%

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Priambodo¹,

Mahmudy²,

Rahman³

2020

KEDS

View full text Add to dashboard Cite

show abstract

“…To implement MKNN, we make sure the validity of training data first before calculating the weight voting [13].…”

Section: Modified K-nearest Neighbor (M-knn)mentioning

confidence: 99%

“…It raised accuracy about 99.25% in selection of feature. Neural network can also be used for classification, for example malware detection case [13], which determine the malware based on the training process of few parameters such as the list of manifest files, permission battery rating, and the size of application file.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Saragih

Fajri

Rakhmandasari

2020

KINETIK

View full text Add to dashboard Cite

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

show abstract

“…• Make bias from input layer to hidden layer ranged between [-β, β] This research uses classical one-hidden-layer neural network architecture. The ideal number of neurons in hidden layer must fullfill equation (5). (Heaton 2005).…”

Section: =1mentioning

confidence: 99%

Rainfall Forecasting Using Backpropagation Neural Network

Sihananto

Mahmudy

2017

JITeCS

View full text Add to dashboard Cite

Rainfall already became vital observation object because it affects society life both in rural areas or urban areas. Because parameters to predict rainfall rates is very complex, using physics based model that need many parameters is not a good choice. Using alternative approach like time-series based model is a good alternative. One of the algorithm that widely used to predict future events is Neural Network Backpropagation. On this research we will use Nguyen-Widrow method to initialize weight of Neural Network to reduce training time. The lowest MSE achieved is {0,02815; 0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer.

show abstract

Android Malware Detection Using Backpropagation Neural Network

Cited by 6 publications

References 4 publications

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Rainfall Forecasting Using Backpropagation Neural Network

Contact Info

Product

Resources

About