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Enhanced Hyperchaotic Image Encryption with CAW Transform and Sea-Lion Optimizer

Journal of Cyber Security and Mobility 2024年第5期13卷 1207-1238页

作者： Abed, Qutaiba Kadhim Al-Jawher, Waleed Ameen Mahmoud Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Uruk University Baghdad Iraq

One of the most effective methods for ensuring data security in the communication and information fields is encryption. There is an important role for multi-chaotic systems in the field of data encryption, due to its wide advantages and its sensitivity to the values of the coefficients and ergodicity. However, some multi-chaotic systems possess low complexity and randomness, which results in unacceptable security behaviour of the current data encryption systems. In this study, we introduce a novel hyperchaotic encryption scheme that enhances image security using a three-phase approach. First, SHA512 is combined with URUK chaos to generate plain-related random sequences. Next, a hybrid CAW transform (Cosine, Arnold, and Wavelet) improves randomness. Finally, the Sea Lion optimization algorithm shuffles pixels to achieve robust encryption. Our experimental results demonstrate that the proposed scheme effectively resists statistical attacks, with superior performance in NPCR, UACI, correlation coefficient, and information entropy tests. © 2024 River Publishers.

关键词： Image enhancement

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A Secure and Efficient Optimized Image Encryption Using Block Compressive Sensing and Logistic Map Method

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Journal of Cyber Security and Mobility 2024年第5期13卷 983-1006页

作者： Abed, Qutaiba Kadhim Al-Jawher, Waleed Ameen Mahmoud Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Uruk University Baghdad Iraq

Recently, multimedia has developed and become very important for transferring images securely through public networks. This paper uses the COOT optimization algorithm with compressive sensing (CS) for image encryption. A good method was proposed for encryption using compressive sensing with COOT optimization and chaos to encrypt images and obtain optimal encryption with the least correlation between pixels. This method will strengthen the encryption against various types of attacks. The natural image was sparsed using discreet wavelet transform (DWT) and the FAN transform. The image is divided into several blocks, and CS is applied to each block. The best measurement matrix was obtained using a COOT-optimized algorithm. All blocks are masked to get the compressed image, and the pixels are quantified. Next, the COOT optimization is used to Shuffle the image pixels to achieve the minimum correlation between the pixels. Then, a logistic map will be used to uniform the image pixel values by diffusion to get the final encrypted image. Chen’s chaotic and logistic map initial values are obtained from the input image after its division into four parts by taking a value from each part. The evaluation results obtained for this algorithm showed that it performs highly compared to other conventional methods. The average PSNR for the reconstructed images was 35.244, the average NPCR and UACI were 90.53 and 29.54, respectively, and the average correlation was (D = 0.0018, V = 0.0031, H = 0.0039). The results proved that the method is strong enough and very efficient to withstand attacks. © 2024 River Publishers.

关键词： Encryption algorithms

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A Hybrid Domain Medical Image Encryption Scheme Using URUK and WAM Chaotic Maps with Wavelet - Fourier Transforms

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Journal of Cyber Security and Mobility 2023年第4期12卷 435-464页

作者： Abdul-Kareem, Ali Akram Al-Jawher, Waleed Ameen Mahmoud Iraqi Commission for Computers and Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Uruk University Baghdad Iraq

Image encryption is one of the most important techniques to maintain data confidentiality against illegal access and fraudulent usage. In this study, a new medical image encryption technique was developed by combining the discrete wavelet transform, the fast Fourier transform, the Arnold transform, and two multidimensional chaotic systems. The medical image is subjected to a discrete wavelet transform before the magic square shuffles the image subbands. Confusion operations are performed on each scrambled subdomain using the Uruk 4D chaotic system. To increase randomness and unpredictability, a second stage of confusion is implemented in the domain of the Fast Fourier transform using the Arnold transform. The final encrypted image is obtained utilizing secret keys derived from the WAM 3D chaotic system. In particular, the initial conditions for chaotic systems are derived from grayscale values, thereby increasing the algorithm's sensitivity to the input image. The results of the tests and the security analysis indicate that the proposed algorithm is exceptionally reliable and secure. © 2023 River Publishers.

关键词： Chaotic systems

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Enhanced Cancer Subclassification Using Multi-Omics Clustering and Quantum Cat Swarm Optimization

Iraqi Journal for Computer Science and Mathematics

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iraqi Journal for computer Science and Mathematics 2024年第3期5卷 552-582页

作者： Ali, Ali Mahmoud Mohammed, Mazin Abed Informatics Institute for Postgraduate Studies Iraqi Commission for Computers & Informatics Baghdad Iraq Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq

Integrating multiple omics data can significantly improve the accuracy of cancer subclassification, a challenging task due to the high dimensionality and limited sample sizes. The integration of these data sets can enhance model performance. This study addresses these challenges by employing Quantum Cat Swarm Optimization (QCSO) for feature selection, along with K-means clustering and Support Vector Machine (SVM) for classification. Using QCSO, the most significant features were identified, resulting in an increase in accuracy from 81% to 100%. Performance was evaluated using accuracy, F1-score, precision, recall, ROC, and the silhouette metric, all of which confirmed the effectiveness of the feature selection approach. Additionally, this method enhances the classification of samples while making the models more interpretable, providing better insights into the molecular mechanisms of cancer. This work contributes to advancing knowledge in the field of cancer research and biology in general. © 2024 College of Education, Al-iraqia University. All rights reserved.

关键词： Support vector machines

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Precise GANs Classification Based on Multi-Scale Comprehensive Performance Analysis

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Journal of Engineering Science and Technology Review 2023年第3期16卷 137-148页

作者： Saeed, Asraa Jalil Hashim, Ahmed A. Iraqi Commission for Computers and Informatics Information Institute for Postgraduate Studies Baghdad Iraq College of Engineering Baghdad Iraq

GAN (Generative adversarial network) is a type of deep learning model that can generate fake data that looks real. GAN consists of two rival neural networks generator and discriminator. There are several types of GAN can be classified based on different criteria such as learning method, network architecture, application, and improvement of training. under the umbrella of learning methods, GAN can be categorized into three types, including unsupervised, semi-supervised, and supervised learning methods. Fully Connected GANs, Convolutional GANs, LAPGANs, AAEs, and Vari GANs are all subtypes of GANs that are distinguished by their respective network architectures, the quantity and composition of layers used in each style distinguishes it from the others. Application areas for GAN include computer vision and image processing, medical imaging, natural language processing, cyber security, and fault detection and tracking. In conclusion, GAN can be categorized in accordance with how training has progressed such as feature matching, probability percentages, regularization, choosing proper optimization, adding noise to discriminator, hyperparameters tuning, normalization, and weight normalization. © 2023 School of Science, IHU. All rights reserved.

关键词： Generative adversarial networks

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Image Encryption Algorithm Based on Arnold Transform and Chaos Theory in the Multi-wavelet Domain

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International Journal of computers and Applications 2023年第4期45卷 306-322页

作者： Abdul-Kareem, Ali Akram Al-Jawher, Waleed Ameen Mahmoud Iraqi Commission for Computers and Informatics Information Institute for Postgraduate Studies Baghdad Iraq Uruk University Baghdad Iraq

Image encryption is essential for ensuring data transmission security over open public networks. Using Multi-Wavelet Transform, Arnold transform, and two chaotic systems, a novel color image encryption technology is designed in this paper. In the proposed algorithm, the primary color components of the input image undergo a multi-wave transform before the Arnold Transform confounds the sub-bands of each color component. Each color component is then divided into blocks shuffled in a predetermined order. Finally, the encrypted image is generated using secret keys derived from Nahrain’s and WAM’s chaotic systems. Notably, the initial conditions of the chaotic maps are generated using image data to increase the algorithm’s sensitivity to the input image. Security analyses conducted to validate the practicability of the new algorithm reveal that it possesses excellent encryption efficiency, high key sensitivity, and the ability to withstand a wide variety of attacks. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cryptography

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Efficient Key Management in Lightweight Cryptography: A New Hashing-Based Approach 8

Efficient Key Management in Lightweight Cryptography: A New ...

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8th International Symposium on Multidisciplinary studies and Innovative Technologies, ISMSIT 2024

作者： Al-Mahmood, Haider H. Alsaad, Saad N. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Mustansiriyah Department of Computer Science College of Science Baghdad Iraq

ISBN: (纸本)9798350354423

Lightweight encryption methods are introduced to overcome the challenges of high-power consumption and high memory usage in the traditional cryptography system. The primary goal of the lightweight encryption technique is to make it suitable for use in devices provided with lower power and small memories. The importance of lightweight cryptosystem is its ability to provide suitable security for data encryption without consuming high level of power and without need for big memories. As the devices with lower battery life and supplied low memory become widespread, encryption key management problems start to emerge. This may lead to a high possibility of an attack on the encryption key and break the encryption system. In this context, this paper proposes a novel hashing- based approach for key generation in lightweight cryptography. The approach involves generating significant hash digits from a seed input to the hashing system, and then using hash code slicing to select different characters and numbers that can be used as a seed for the key generating system. The practical results, which demonstrate that a small seed length can effectively generate a large key length, are a testament to the effectiveness of the proposed approach. These results can be used in the encryption process to reduce the initial total bits, thereby enhancing the efficiency of the system. © 2024 IEEE.

关键词： Cryptographic Algorithms Encryption Key Management Hashing Techniques IoT Security Lightweight Cryptography

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Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

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International Journal of Intelligent Systems Technologies and Applications 2024年第1期22卷 29-40页

作者： Hussein, Raed Mohammed George, Loay E. Miften, Firas Sabar Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq Computer Sciences Department University of Information Technology and Communication Baghdad Iraq Computer Sciences Department College of Education for Pure Science University of Thi-Qar Thi-Qar Iraq

Driver drowsiness is one of the leading causes of road deaths and transportation industry dangers. Due to its direct evaluation of neurophysiological brain activity, electroencephalography (EEG) has been regarded as one of the most reliable physiological indicators for identifying driver drowsiness. This study proposes a straightforward, cost-effective method for detecting driver drowsiness using a single channel. The contribution of this research is the discovery of drowsiness using discriminated features [moments features (M1, M2, M3, M4), roughness features (R1, R2, R3, R4), zero crossing rate (ZCR), sample entropy (SE) and median absolute deviation (MAD)] from publicly available datasets. A novel model was introduced in this study, which involved the fusion of wavelet transform Daubechies order 4 (WTDB4) and residue decomposition (RD) techniques for feature extraction. Various classification algorithms, including the least-square support vector machine (LSSVM) and ensemble models were compared in terms of their performance metrics. The algorithm that exhibited superior accuracy with reduced computational time was chosen to classify the driver's status into two groups: awake and drowsy. Notably, the proposed model achieved an impressive accuracy of 97.95%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electroencephalography

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Digital Image Forgery Detection Based on Convolutional Neural Networks 6th

Digital Image Forgery Detection Based on Convolutional Neura...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Saleh, Noha M. Naji, Sinan A. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9789819774227

Image authentication has become a hot topic with the currently available technology for manipulating and distributing images. Image authentication aims to ensure the authenticity of digital images and automatically detect forged images that have been tampered with after they have been captured. This paper presents a convolutional network (CNN) model for detecting forged images. The proposed model implies three main stages: First, preprocessing the input image by adopting logarithmic mapping to refine the quality of the extracted features, especially in dark regions. Secondly, a novel CNN architecture was trained to classify arbitrary images into two categories: "original" and "forged". The CNN model locates some descriptors as a descriptor map. Finally, the model finds the similarities and dependencies between these features. The proposed model was tested and evaluated on three datasets under various copy-move conditions. Experimental results reveal that the model can detect forged images with high accuracy, reaching up to 97.11%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A classifier design for micro bubble generators based on deep learning technique

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Materials Today: Proceedings 2023年 80卷 2684-2696页

作者： Qaddoori, Ayman S. Saud, Jamila H. Hamad, F.A. Informatics Institute for Postgraduate Studies Iraqi Commission for Computer and Informatics Baghdad Iraq College of Science Mustansiriyah University Baghdad Iraq School of computing Engineering and Digital Technology Teesside University Middlesbrough United Kingdom

This paper aims to study the sizes of bubbles generated by the micro-bubble generator device (MBGs) in the water because of its impact on the dissolved oxygen percentage in the water and we find this in aquaculture where oxygen is important for marine life and in many applications. Where we have designed a classifier for micro-bubbles of different sizes and the effectiveness of the device has been proven in generating bubbles whose size ranges from 20 to 50 and calculating their numbers in the image by converting the image from (RGB) to (HSV), it has proven its effectiveness in maintaining lighting and neglecting color information Which is not important in identifying bubbles in the image, as well as increasing the processing speed, and then finding edges in the image, using canny edge detection, which has proven its great effectiveness in finding weak and strong edges. Where we then used the (Circular Hough Transform) algorithm to identify the tiny bubbles in the image, calculate their average diameter and numbers in the image, and find the overlapping bubbles between them by using (Two Thresholds) one for the edge and the other for the center of the bubble and then making (Segmentation) for these bubbles and inserting them on (Gaussian Normal Distribution) Do not neglect the dark bubbles, because in most cases they are unreal bubbles and may be distortions in the image or reflections in the lighting. And then make (zero paddings) for the extracted images to make them equal in size and avoid the distortion that will happen in the image if another method is used to make all bubbles of equal dimensions to prepare them for classification. And then we calculated the (mean square error) of the resulting bubble images to avoid repeating the same bubble in (datasets). The efficiency of the classifier was calculated by using deep learning technology. Where we built a (Convolution Neural Network) consisting of (15) layers and then we trained these layers on the images resul

关键词： Feature extraction

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