Preferred Language
Articles
/
lBZirIoBVTCNdQwC8qJ0
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
...Show More Authors
Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet attacks using CICIDS2017 dataset. The proposed model designed based on two types of filters to the botnet features; Correlation Attribute Eval and Principal Component deployed to reduce the dataset dimensions and to decrease the time complexity of the botnet detection process. The detection enhancement achieved by reducing the features of the dataset from 85 to 9. The training stage of classifiers is developed and compared based on six classifiers called (Random Forest, IBK, JRip, Multilayer Perceptron, Naive Bayes and OneR) evaluated to accomplish an optimized detection model. The performance and results of the proposed framework are validated using well-known metrics such as Accuracy (ACC), Precision (Pr), Recall (Rc) and F-Measure (F1). The consequence is that the combination of Correlation Attribute Eval (filter) with JRip (classifier) together can satisfy significant improvement in the Botnet detection process using CICIDS2017 dataset.</p>
Scopus Crossref
View Publication
Publication Date
Wed Aug 11 2021
Journal Name
International Journal Of Interactive Mobile Technologies (ijim)
Image Denoising Using Multiwavelet Transform with Different Filters and Rules
...Show More Authors

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by usi

... Show More
View Publication
Scopus (4)
Crossref (2)
Scopus Crossref
Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
An Overview of Audio-Visual Source Separation Using Deep Learning
...Show More Authors

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

... Show More
View Publication Preview PDF
Scopus (1)
Crossref (1)
Scopus Crossref
Publication Date
Sat Mar 29 2014
Journal Name
International Journal Of Academic Research In Progressive Education And Development
The Effects of Problem-Based Learning on Self-Directed Learning Skills among Physics Undergraduates
...Show More Authors

The aim of this study is to compare the effects of three methods: problem-based learning (PBL), PBL with lecture method, and conventional teaching on self-directed learning skills among physics undergraduates. The actual sample size comprises of 122 students, who were selected randomly from the Physics Department, College of Education in Iraq. In this study, the pre- and post-test were done and the instruments were administered to the students for data collection. The data was analyzed and statistical results rejected null hypothesis of this study. This study revealed that there are no signifigant differences between PBL and PBL with lecture method, thus the PBL without or with lecture method enhances the self-directed learning skills bette

... Show More
Publication Date
Mon Dec 14 2020
Journal Name
2020 13th International Conference On Developments In Esystems Engineering (dese)
Anomaly Based Intrusion Detection System Using Hierarchical Classification and Clustering Techniques
...Show More Authors

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

... Show More
View Publication
Scopus (3)
Crossref (2)
Scopus Clarivate Crossref
Publication Date
Mon Jan 09 2023
Journal Name
2023 15th International Conference On Developments In Esystems Engineering (dese)
Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform
...Show More Authors

View Publication
Scopus (7)
Crossref (5)
Scopus Crossref
Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
3D scenes semantic segmentation using deep learning based Survey
...Show More Authors
Abstract<p>Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the po</p> ... Show More
View Publication
Scopus (1)
Crossref (1)
Scopus Crossref
Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network
...Show More Authors

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

... Show More
View Publication Preview PDF
Scopus (9)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Sun Jul 01 2018
Journal Name
Journal Of Aerosol Science
On the design of miniature parallel-plate differential mobility classifiers
...Show More Authors

View Publication
Scopus (5)
Crossref (5)
Scopus Clarivate Crossref
Publication Date
Sun May 01 2016
Journal Name
2016 Al-sadeq International Conference On Multidisciplinary In It And Communication Science And Applications (aic-mitcsa)
Landsat-8 (OLI) classification method based on tasseled cap transformation features
...Show More Authors

View Publication
Scopus (4)
Crossref (3)
Scopus Crossref
Publication Date
Mon Nov 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Enhancing the human resources quality by adopting an adventure learning method in their development
...Show More Authors

The research aims to identify how to enhance the quality of the human resources, focusing on four dimensions (efficiency, effectiveness, flexibility, and reliability), by adopting an adventure learning method that combines theoretical and applied aspects at the same time, when developing human resources and is applied using information technology, and that Through its dimensions, which are (cooperation, interaction, communication, and understanding), as the research problem indicated a clear deficiency in the cognitive perception of the mechanism of employing adventure learning dimensions in enhancing human resources quality, so the importance of research was to present treatments and proposals to reduce this problem. To achieve

... Show More
View Publication Preview PDF
Crossref