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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Zaid Alaa Hussien
Southern Technical University
Computer Science & IT Electrical & Electronics Engineering

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Method to implement K-NN machine learning to classify data privacy in IoT environment Qahtan Makki Shallal; Zaid Alaa Hussien; Alaa Ahmed Abbood
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp985-990

Abstract

Internet of Things technology allows many devices to connect with each other. The interaction could be between humans and devices or between devices itself. In fact, the data are traveling between the devices through the media within the boundary, and it could be traveling outside the boundary when it required to be analyzed or stored in the cloud through the internet. Due the transmission media and internet, the data are vulnerable to attacks. Thus, the data need to be encrypted strongly for the purpose of protection. Usually, most of the encryption techniques will consume computer resources. In this work, we divide the data that are used in the IoT environment into three levels of sensitivity which are low, medium and high sensitive data to leverage the computer resources such as time of encryption and decryption, battery usage and so on. A framework is proposed in this work to encrypt the data depends on the level of sensitivity using the machine learning K nearest neighbors (K-NN).
Ensuring Data Integrity Scheme Based on Digital Signature and Iris Features in Cloud Salah H. Abbdal; Thair A. Kadhim; Zaid Ameen Abduljabbar; Zaid Alaa Hussien; Ali A. Yassin; Mohammed Abdulridha Hussain; Salam Waley
Indonesian Journal of Electrical Engineering and Computer Science Vol 2, No 2: May 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v2.i2.pp452-460

Abstract

Cloud computing is a novel paradigm that allows users to remotely access their data through web- based tools and applications. Later, the users do not have the ability to monitor or arrange their data. In this case, many security challenges have been raised. One of these challenges is data integrity. Contentiously, the user cannot access his data directly and he could not know whether his data is modified or not. Therefore, the cloud service provider should provide efficient ways for the user to ascertain whether the integrity of his data is protected or compromised. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. Additionally, we propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the iris feature extraction and digital signature.  Iris recognition has become a new, emergent approach to individual identification in the last decade. It is one of the most accurate identity verification systems. This technique gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user’s iris features to secure and integrate data in a manner difficult for any internal or external unauthorized entity to take or compromise it. Iris recognition is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane. Extensive security and performance analysis show that our proposed scheme is highly efficient and provably secure.
Securing audio transmission based on encoding and steganography Enas Wahab Abood; Zaid Ameen Abduljabbar; Mustafa A. Al Sibahee; Mohammed Abdulridha Hussain; Zaid Alaa Hussien
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1777-1786

Abstract

One of the things that must be considered when establishing a data exchange connection is to make that communication confidential and hide the file’s features when the snoopers intercept it. In this work, transformation (encoding) and steganography techniques are invested to produce an efficient system to secure communication for an audio signal by producing an efficient method to transform the signal into a red–green–blue (RGB) image. Subsequently, this image is hidden in a cover audio file by using the least significant bit (LSB) method in the spatial and transform domains using discrete wavelet transform. The audio files of the message and the cover are in *.wav format. The experimental results showed the success of the transformation in concealing audio secret messages, as well the remarkability of the stego signal quality in both techniques. A peak signal-to-noise ratio peak signal-to-noise ratio (PSNR) scored (20-26) dB with wavelet and (81-112) dB with LSB for cover file size 4.96 MB and structural similarity index metric structural similarity index metric (SSIM) has been used to measure the signal quality which gave 1 with LSB while wavelet was (0.9-1), which is satisfactory in all experimented signals with low time consumption. This work also used these metrics to compare the implementation of LSB and WAV.
Fully automated model on breast cancer classification using deep learning classifiers Mudhafar Jalil Jassim Ghrabat; Zaid Alaa Hussien; Mustafa S. Khalefa; Zaid Ameen Abduljabbar; Vincent Omollo Nyangaresi; Mustafa A. Al Sibahee; Enas Wahab Abood
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp183-191

Abstract

Deep learning models on the same database have varied accuracy ratings; as such, additional parameters, such as pre-processing, data augmentation and transfer learning, can influence the models’ capacity to obtain higher accuracy. In this paper, a fully automated model is designed using deep learning algorithm to capture images from patients and pre-process, segment and classify the intensity of cancer spread. In the first pre-processing step, pectoral muscles are removed from the input images, which are then downsized. The removal of pectoral muscles after identification may become crucial in classification systems. Finally, the pectoral musclesaredeleted from the picture by using an area expanding segmentation. All mammograms are downsized to reduce processing time. Each stage of the fully automated model uses an optimisation approach to obtain highaccuracy results at respective stages. Simulation is conducted to test the efficacy of the model against state-of-art models, and the proposed fully automated model is thoroughly investigated. For a more accurate comparison, we include the model in our analysis. In a nutshell, this work offers a wealth of information as well as review and discussion of the experimental conditions used by studies on classifying breast cancer images.
Provably secure and efficient audio compression based on compressive sensing Enas Wahab Abood; Zaid Alaa Hussien; Haifaa Assy Kawi; Zaid Ameen Abduljabbar; Vincent Omollo Nyangaresi; Junchao Ma; Mustafa A. Al Sibahee; Saad Ahmad Ali Kalafy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp335-346

Abstract

The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.
Co-Authors Alaa Ahmed Abbood Ali A. Yassin Enas Wahab Abood Haifaa Assy Kawi Junchao Ma Mohammed Abdulridha Hussain Mudhafar Jalil Jassim Ghrabat Mustafa A. Al Sibahee Mustafa A. Al Sibahee Mustafa S. Khalefa Qahtan Makki Shallal Saad Ahmad Ali Kalafy Salah H. Abbdal Salam Waley Thair A. Kadhim Vincent Omollo Nyangaresi Vincent Omollo Nyangaresi Zaid Ameen Abduljabbar Zaid Ameen Abduljabbar