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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Ammar D. Jasim
Al-Nahrain University
Computer Science & IT

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Half Gaussian-based wavelet transform for pooling layer for convolution neural network Aqeel M. Hamad Alhussainy; Ammar D. Jasim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16398

Abstract

Pooling methods are used to select most significant features to be aggregated to small region. In this paper, anew pooling method is proposed based on probability function. Depending on the fact that, most information is concentrated from mean of the signal to its maximum values, upper half of Gaussian function is used to determine weights of the basic signal statistics, which is used to determine the transform of the original signal into more concise formula, which can represent signal features, this method named half gaussian transform (HGT). Based on strategy of transform computation, Three methods are proposed, the first method (HGT1) is used basic statistics after normalized it as weights to be multiplied by original signal, second method (HGT2) is used determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian, while the third method (HGT3) is worked in similar to (HGT1) except, it depend on entire signal. The proposed methods are applied on three databases, which are (MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results show that, our methods are achieved good improvement, which is outperformed standard pooling methods such as max pooling and average pooling.
Performance analysis of intrusion detection for deep learning model based on CSE‑CIC‑IDS2018 dataset Baraa Ismael Farhan; Ammar D. Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1165-1172

Abstract

The evolution of the internet of things as a promising and modern technology has facilitated daily life. Its emergence was accompanied by challenges represented by its frequent exposure to attacks and its being a target for intruders who exploit the gaps in this technology in terms of the nature of its heterogeneous data and its large quantity. This made the study of cyber security an urgent necessity to monitor infrastructures It has network flaw detection and intrusion detection that helps protect the network by detecting attacks early and preventing them. As a result of advances in machine learning techniques, especially deep learning and its ability to self-learning and feature extraction with high accuracy, the research exploits deep learning to analyze the real data set of CSE-CIC-IDS2018 network traffic, which includes normal behavior and attacks, and evaluate our deep model long short-term memory (LSTM), That achieves accuracy of detection up to 99%.
A novel pooling layer based on gaussian function with wavelet transform Aqeel M. Hamad alhussainy; Ammar D. Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1289-1298

Abstract

Convolution represent basic layer in the convolutional neural network, but it can result in big size of the data, which may increase the complexity of the network. Different pooling methods are used to perform down sample these data. In this paper, we have proposed a novel pooling method by using Gaussian function to determine the wavelet filter coefficients. At first, the basic statistics are determined for each pool size of the signal, then Gaussian probability distribution function is determined. According to the procedure of extracting the features, three methods are proposed, the first method is used the normalized values of basic statistics as wavelet filter to be multiplied by original signal, the second method used the determined statistics as features of the original signal, then multiplied it with constant wavelet filter based on Gaussian, while the third method is similar to first method, except it depend on entire signal instead of each pool size. The proposed methods are combined with other standard methods such as max and pooling. The experiments are performed on different datasets and the results show that the proposed methods perform or outperform other methods and can increase performance of the (CNN).
Intelligent malware classification based on network traffic and data augmentation techniques Ammar D. Jasim; Rawaa Ismael Farhan
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp903-908

Abstract

To prevent detection, attackers frequently design systems to rearrange and rewrite their malware automatically. The majority of machine learning techniques are not sufficiently resistant to such re-orderings because they develop a classifier based on a manually created feature vector. Deep learning techniques like convolutional neural networks (CNN) have lately proven to perform better than more traditional learning algorithms, especially in applications like picture categorization. As a result of this success, CNN network proposed with data augmentation techniques (to enhance the performance) to classify malware samples. We trained a CNN to classify the photos using converted grayscale images from malware files. Our methodology outperforms other methods with an accuracy of 98.80%, according to experimental results.
Co-Authors Aqeel M. Hamad Alhussainy Baraa Ismael Farhan Rawaa Ismael Farhan