مجلة الكاظم لعلوم الحاسوب
type: channel
description: It is a free journal published by Imam Al-Kadhim university College (IKU), Baghdad, Iraq. It is open access in computer science. The publication of this journal contributes to the development of science and its impact. In addition, it provides a means of discussion for science researchers.
publisher: Imam Al-Kadhim university College (IKU)
language: ar
publication name: مجلة الكاظم لعلوم الحاسوب
issn: 3007-1429
items: Cyberdefense Powered by Generative AI: A Comprehensive State –of- the -Art Review
items: Intelligent Achievement of Participation in E-Governance Using Machine Learning: A Socio-Technical Approach to Programming Language Education
items: ستار شاكر محمد الكشف عن الوجوه المزيفة باستخدام خوارزميات التعلم العميق
items: إطار عمل عالي الأداء للتعلم الهجين لتعقيد الإنتروبيا من أجل الكشف القوي عن الهجمات الإلكترونية الخفية
items: A Lightweight Hybrid Encryption Algorithm for Internet of Things Security using ASCON and AES with Chaotic System
items: Scalable Video Encryption for Real-Time Applications Using Parallel Computing Models
items: Deep Learning Models In The Early Brediction Of Epileptic Seizures
items: Comparative Evaluation of Machine Learning Techniques for Handwritten Digit Classification Using the MNIST Dataset
items: A practical study to achieve the Predictive GIS-Based Decision Support System for Forecasting Teacher Demand and Optimizing Educational Human Resource
items: Chicken Swarm Optimization based QoS Web Service Composition in Cloud Computing Environment
items: تحليل تدهور القرار الذكي في ظل التوسع الشديد للبيانات في نظم معلومات المؤسسات
items: علوم الحاسوب تحليلات الأداء المدعومة بالذكاء الاصطناعي للعاملين في مجال التعليم: دراسة تجريبية في المدارس العراقية
items: دمج الصور متعددة البؤر باستخدام خوارزمية جينية، مع مراعاة تباين شدة الإضاءة المحلية وخصائص الحواف.
items: Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP
items: A Hybrid CNN–ViT Deep Learning Framework for Detecting and Classifying Image Manipulation in Online News Media
items: Twitter Bot Detection Using Relational Graph Convolutional Networks and Convolutional Neural Networks
items: A Hybrid AI Model for Early Detection of Heart Disease Using Clinical and ECG Data
items: Experimental Evaluation of Adaptive Multi-Agent AI Models for Detecting Stealthy Cyber Attacks in Dynamic Network Environments based on MADDPG
items: Developing an Enhanced IHBO Algorithm with Chaotic Binary Hashing to Optimize BiGRU for Industry 4.0 IIoT Anomaly Detection
items: Federated Learning for Early Detection of Advanced Persistent Threats in IoT Networks
items: Practical Implementation and Analysis of Software Metrics Impact on Maintainability in Open-Source Systems
items: Explainable Artificial Intelligence Integrated Ensemble Learning Framework for Diabetes Prediction
items: Optimized Hybrid CNN-LSTM Framework with Multi-Feature Analysis and SMOTE for Intrusion Detection in SDN
items: Computationally Efficient Hybrid Framework for MNIST Handwritten Digit Recognition
items: The Connected Classroom: Leveraging EdTech to Enhance Student Engagement
items: Deep Learning-Based Blood Cell Classification with Enhanced Data Preprocessing and Augmentation
items: Cognitive Honeypots AI-Enhanced Deception for Proactive Threat Hunting
items: Analyzing Big Data to Mitigate Cyber Attacks Using Machine Learning Classifications: A Comparative Study of an Efficient Classifier Set
items: Multi-Dimensional Deep Learning for Unusual Detection in Security Footage: A 3D CNN, LSTM, and Attention-Based Approach
items: Pepper Leaf Disease Detection Using Deep Learning Techniques

Cyberdefense Powered by Generative AI: A Comprehensive State –of- the -Art Review
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description: Cyberattacks are becoming more advanced, challenging the traditional defenses based on signature-based and rule-based approaches. The current literature on generative artificial intelligence (GenAI) in the context of cybersecurity is disjointed based on model family or area of application, and it does not combine technical performance indicators with practical implementation limitations, making a comprehensive picture impossible. To fill this gap, the study conducts a comprehensive review of peer-reviewed articles and conference papers (2021-2026) indexed in Scopus and ScienceDirect, and Springer databases, involving the use of GenAI as a defensive cybersecurity tool. These papers are divided into (I) GenAI model family: generative adversarial networks (GAN), large language models (LLM), variational autoencoders (VAE), diffusion models, and hybrid GenAI; (II) application domain: intrusion detection, malware detection, anomaly detection, threat intelligence, privacy-preservation, vulnerability detection, and phishing and spamming detection; and (III) defense strategy: reactive, proactive, and adversarial. GenAI typically increases the accuracy of detection and data efficiency and provides active defense. Nevertheless, the practical validation is usually limited to offline tests that apply imprecise metrics. The paper provides the performance–efficacy trade-off model, which relates technical standards and realistic implementation limitations. It also identifies a research roadmap in the future focused on creating autonomous, privacy-protective, and trustful GenAI-powered cyber defenses and suggests a living review platform to keep track of advances in the fast-changing area.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Ahmed Alsamman
creator: Najla Badie Al Dabagh
rights: الحقوق الفكرية (c) 2026 Ahmed Alsamman, Najla Badie Al Dabagh (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 82
ending page: 108
doi: 10.61710/9jq4y549

Intelligent Achievement of Participation in E-Governance Using Machine Learning: A Socio-Technical Approach to Programming Language Education
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description: *]:pointer-events-auto R6Vx5W_threadScrollVars scroll-mb-[calc(var(--scroll-root-safe-area-inset-bottom,0px)+var(--thread-response-height))] scroll-mt-(--header-height)" dir="auto" data-turn-id="4de869c3-4e14-4a05-a57b-65846f446ddd" data-turn-id-container="4de869c3-4e14-4a05-a57b-65846f446ddd" data-testid="conversation-turn-1" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto [content-visibility:auto] supports-[content-visibility:auto]:[contain-intrinsic-size:auto_100lvh] R6Vx5W_threadScrollVars scroll-mb-[calc(var(--scroll-root-safe-area-inset-bottom,0px)+var(--thread-response-height))] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" data-turn-id="request-WEB:dc20150d-0629-4d26-a294-22159a73358a-0" data-turn-id-container="request-WEB:dc20150d-0629-4d26-a294-22159a73358a-0" data-testid="conversation-turn-2" data-scroll-anchor="false" data-turn="assistant"> تستكشف هذه الورقة البحثية كيفية دمج مبادئ الحوكمة الإلكترونية وتقنيات التعلّم الآلي (ML) لدعم التصميم التشاركي في الأنظمة التعليمية، وبشكل خاص في مناهج تعليم لغات البرمجة. واستنادًا إلى نظرية الأنظمة الاجتماعية-التقنية لـ Enid Mumford ومنهجية ETHICS، تمكّن الباحثون من اقتراح إطار عمل جديد يستخدم ثلاثة مصنّفات خاضعة للإشراف في التعلّم الآلي، وهي: آلة متجهات الدعم (SVM)، والغابة العشوائية (RF)، وشجرة القرار (DT)، وذلك لتحليل بيانات استبيانات المعلمين التي جُمعت عبر قنوات الحوكمة الإلكترونية بصورة منهجية. تضمّنت مجموعة البيانات إجابات 94 معلّمًا في بغداد، العراق، واستُخدمت لتصنيف احتياجات تحسين العملية التعليمية إلى ست فئات هي: تطوير الأجهزة (DH)، تطوير أدوات العرض والتوضيح (DDT)، تطوير خبرات المعلمين (DTE)، إعادة توزيع الحصص التدريسية للمعلمين (RDTS)، تطوير المنهج الدراسي (DS)، وتطوير قدرات الطلبة (DSA). وأظهرت نتائج التجربة أن نموذج آلة متجهات الدعم (SVM) حقق أعلى دقة تصنيف، حيث بلغت 99.90٪ في مرحلة التدريب و98.00٪ في مرحلة الاختبار. كما بيّن تحليل نتائج الاستبيانات أن أكثر مجالات التحسين إلحاحًا هي تطوير المنهج الدراسي بنسبة 85٪، وتطوير قدرات الطلبة بنسبة 75٪. وتبرز …
subject: Computer Science
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subject: علوم الحاسوب
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creator: shakir Fadhil Shabram
rights: الحقوق الفكرية (c) 2026 shakir Fadhil Shabram (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 153
ending page: 162
doi: 10.61710/g7bwn706

ستار شاكر محمد الكشف عن الوجوه المزيفة باستخدام خوارزميات التعلم العميق
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description: الملخص: مع التطور السريع لتقنيات توليد الوجوه في السنوات الأخيرة، أصبحت هذه التقنيات قادرة على إنشاء صور واقعية مذهلة للوجوه البشرية، مما يطمس الحدود بين الحقيقي والمزيف، ويجعل من الصعب على الناس التمييز بينهما. ورغم استخداماتها الإيجابية في مجالات مثل التعليم والترفيه والفنون وإنشاء محتوى هادف، إلا أن احتمالية إساءة استخدام هذه التقنيات عالية جدًا، لا سيما على وسائل التواصل الاجتماعي. قد يؤدي ذلك إلى انتشار معلومات زائفة ومضللة تهدد الثقة في المحتوى الرقمي، مثل استخدام الوجوه المزيفة في الاحتيال الإلكتروني أو انتحال الهوية. وهذا يستدعي تطوير حلول فعالة لكشفها. تقترح هذه الورقة البحثية نظامًا لتصنيف صور الوجوه باستخدام خوارزميات الشبكات العصبية الالتفافية (CNN). نظرًا لفشل أنظمة التعلم الآلي التقليدية، التي تعتمد على استخراج الميزات يدويًا، وظهور أنماط أكثر تعقيدًا يصعب فهمها أو تمثيلها بسهولة، فإن هذه الأنظمة غير قادرة على التعميم على بيانات غير مرئية. علاوة على ذلك، تتأثر هذه الأنظمة بالتغيرات في البيانات أو التشويش، مما يضعف أدائها. حقق نظامنا نتائج ممتازة، حيث بلغ متوسط ​​دقة التنبؤ 99.89% على مجموعة بيانات rvf10k. هذه الدقة العالية تجعله بديلاً جيداً للأنظمة السابقة.
subject: Computer Science
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subject: علوم الحاسوب
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creator: satar Shaker
rights: الحقوق الفكرية (c) 2026 satar Shaker (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 136
ending page: 152
doi: 10.61710/9pxtfj78

إطار عمل عالي الأداء للتعلم الهجين لتعقيد الإنتروبيا من أجل الكشف القوي عن الهجمات الإلكترونية الخفية
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description: تتطور الهجمات الإلكترونية نحو سلوكيات سرية تتجاوز أنظمة الكشف التقليدية القائمة على التوقيعات وأساليب الكشف عن الشذوذ أحادية المقياس. تقترح هذه الورقة البحثية إطار عمل هجينًا للتعلم القائم على الإنتروبيا والتعقيد (HECL) للكشف الفعال عن الهجمات الإلكترونية بالاعتماد على نماذج معلومات الشبكات. يحلل النهج المقترح بشكل تعاوني مخطط شانون القائم على الإنتروبيا، والتعقيد الإحصائي، وقابلية التكيف للتعلم لتوصيف أنشطة المرور الطبيعية وغير الطبيعية. وبخلاف إنتروبيا الكشف التقليدية القائمة على الأنظمة، تستخدم الطريقة المقترحة الترابط الزمني بين كل من الإنتروبيا والتعقيد، مما يسمح بمطابقة الهجمات منخفضة المعدل والهجمات التي تُخفي الهجمات. تُظهر عمليات المحاكاة الشاملة أن النموذج المقترح يحقق دقة كشف فائقة، وتقاربًا سريعًا، وانخفاضًا في الإنذارات الكاذبة القائمة على المعدل عند مقارنته بالإنتروبيا المستقلة والتعلم الآلي القائم على الأساليب. تؤكد النتائج الرئيسية أن دمج نظام قائم على نظرية المعلومات مع المؤشرات القائمة على التعلم يوفر تقنية أمنية فعالة وخفيفة الوزن، مناسبة لأنظمة أمن المعلومات الحديثة.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Zaydon L.Ali
rights: الحقوق الفكرية (c) 2026 Zaydon L.Ali (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 68
ending page: 81
doi: 10.61710/krmqgn94

A Lightweight Hybrid Encryption Algorithm for Internet of Things Security using ASCON and AES with Chaotic System
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description: The recent rise in the use of Internet of Things (IoT) devices has further increased the pressure on cryptographic solutions that will meet the challenge of high-security standards, yet nearly meet the extreme resource requirements imposed by embedded computers by nature. The paper introduces a new hybrid encryption architecture that is aimed at meeting these conflicting requirements of the IoT setting. The scheme suggested is a variant of AES-r cipher combined with the ASCON authenticated-encryption primitive giving the AESASCON algorithm, and extended with integrity verification by the SHA3-256 hash to create the AESASCONH construction. Stochastic key material is calculated using a four-dimensional NoseSprott-Jerk-Rossler (4D-NSJR) chaotic system, which is highly sensitive to initial conditions causing the effective key space to become large and making brute-force enumeration challenging. The architecture is segmented into three layers that collaborate with one another and they are chaotic key generation, symmetric data encryption and authenticated-integrity verification. General testing against the complete NIST SP 800-22 statistical test is done that indicates that all the 15 randomness tests have been passed. Experiments with Five data sizes are used in experiments to demonstrate speedups ranging from 42% for large data payloads (1.5 MB) to 175% for small data sizes compared to AES. This demonstrates that the suggested approach provides efficient and secure protection for IoT. and entropy values of data of up to about the theoretical maximum of 8.0 bits per symbol, and thus confirm speed improvements as well as cryptographic strength is available to support high-assurance IoT deployments.
subject: Computer Science
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subject: علوم الحاسوب
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creator: wasan alaa Hussain
rights: الحقوق الفكرية (c) 2026 wasan alaa Hussain (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 109
ending page: 123
doi: 10.61710/pghstr62

Scalable Video Encryption for Real-Time Applications Using Parallel Computing Models
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description: Abstract It is necessary in various real-time applications such as teleconferencing, live streaming, and remote monitoring. However, applying standard encryption methods on high resolution video streams can cause computational delay. We propose a scalable video encryption framework, based on AES-256 and multi-threaded parallel processing to boost the performance of cryptography without compromising security in the paper. Video frames are sliced into independent chunks and assigned to available CPU cores so that encryption can occur in parallel. In order to recover frame ordering correctly, we use a synchronization mechanism. Extensive experimental evaluation on Full HD video streams shows that the proposed framework improves throughput and results in a four-times reduction in encryption time when compared to sequential execution. Using eight threads with small CPU utilization efficiency, the system achieved a speed up as high as 4.62×. These results show that parallel software encryption on CPU is a realistic method for secure real-time video applications.
subject: Computer Science
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subject: علوم الحاسوب
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creator: baydaa alkhafaji
creator: suad abed
creator: maha talib
creator: israa akram
creator: wasan abed
rights: الحقوق الفكرية (c) 2026 baydaa alkhafaji, Suad abed alwahab,, Maha talib Abdullah, Israa Akram Alzuabidi, Wasan abed alwahab (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 124
ending page: 135
doi: 10.61710/9dfm5t34

Deep Learning Models In The Early Brediction Of Epileptic Seizures
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description: ملخص لا يزال التنبؤ المبكر بنوبات الصرع يمثل تحديًا في علم الأعصاب السريري نظرًا لعدم القدرة على التنبؤ بالنوبات وما تنطوي عليه من مخاطر نفسية وجسدية على المريض. يُمكن استخدام تخطيط كهربية الدماغ (EEG) كأداة مهمة لأنظمة الإنذار المبكر بالنوبات بهدف تحسين جودة حياة المريض. لذا، تُقارن هذه الورقة البحثية وتقيّم أداء عدة نماذج للتعلم العميق للتنبؤ بالنوبات قبل 15 دقيقة باستخدام بيانات تخطيط كهربية الدماغ. ولتحقيق هذه الغاية، تم اتباع بروتوكول تجريبي موحد لضمان المقارنة بين النماذج: LSTM، وBiLSTM، وشبكة الرسم البياني العصبية، وTransformer. تم تقييم النماذج بناءً على قاعدة بيانات CHB-MIT التي تضم تسجيلات طويلة الأمد من 24 مريضًا. خضعت كل إشارة لخطوات معالجة مسبقة مختلفة مثل الترشيح والتطبيع وتقسيم النافذة الزمنية. بعد ذلك، تم استخراج مجموعة فردية من الميزات، مع موازنة البيانات بين فئتي ما قبل النوبة والنوبة. دُرست النماذج بناءً على مقاييس متنوعة مثل الدقة والنوعية وقابلية التكرار ومعامل F1 والمساحة تحت المنحنى. لذا، تُظهر نتائج نماذج التعلم العميق المستخدمة أداءً متقاربًا. فقد أظهر نموذجا LSTM وBiLSTM دقة عالية في الحالات الإيجابية، بمعدل استرجاع بلغ 0.99. أما نموذج Transformer، فقد حقق أعلى دقة بلغت 0.95 وأعلى خصوصية بلغت 0.76. في حين أظهر نموذج الشبكة العصبية البيانية (GNN) أداءً مماثلاً للنماذج الأخرى، بأداء تنافسي ولكنه لم يتفوق عليها جميعًا. وقد مكّن استخدام الذكاء الاصطناعي القابل للتفسير (XAI) من تقييم أهمية المراحل الزمنية والخصائص في كلٍ من أداء النموذج وقراراته. وبناءً على ذلك، تؤكد النتائج أن نموذج Transformer يتمتع بقدرة عالية على التنبؤ المبكر بالنوبات، وبالتالي، فهو مفيد في تطوير أنظمة إنذار مبكر موثوقة لمرضى الصرع.
subject: Computer Science
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subject: علوم الحاسوب
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creator: shahad sallah
rights: الحقوق الفكرية (c) 2026 shahad sallah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 22
ending page: 36
doi: 10.61710/kjcs.v4i1.149

Comparative Evaluation of Machine Learning Techniques for Handwritten Digit Classification Using the MNIST Dataset
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description: Machine learning (ML) and deep learning (DL) have advanced at a very rapid pace; thus, improving image classification in many fields. MNIST data is one of the most popular benchmark datasets to compare classification algorithms and it is represented by 70,000 grayscale images of handwritten digits (09). This paper will make a comparative analysis of a number of machine learning and deep learning algorithms and models, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) using the MNIST dataset. All of the models were trained and tested using the same preprocessing methods, including normalization and feature scaling. Several evaluation metrics were applied to evaluate the performance, and they included accuracy, precision, recall, F1-score, and confusion matrix. The findings prove that conventional machine learning models, especially SVM and Random Forest, perform competitively. Nonetheless, CNN is more effective as compared to classical models because it is capable of learning hierarchical features of space automatically using image data. The results indicate the significance of choosing suitable models depending upon the nature of data and computing limitations. This paper presents a comparative study in detail and in a systematic manner that would improve the comprehension of the trade-offs between the model performance and computational efficiency and could act as a valuable source of information to researchers and practitioners in image classification exercises.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Aysar Tuaimah
rights: الحقوق الفكرية (c) 2026 Aysar Tuaimah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 58
ending page: 67
doi: 10.61710/2t8bpy67

A practical study to achieve the Predictive GIS-Based Decision Support System for Forecasting Teacher Demand and Optimizing Educational Human Resource
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description: Unavoidably, educators world over are struggling to address the issue of supply and demand of teachers in geographically dispersed regions. The conventional workforce planning methods are based on reactive, manual and non-spatial forecasting processes which in most cases leads to shortage of teachers in the rural sections and excesses in the urban centres. In this paper, the author suggests a Predictive Geographic Information System (GIS)-Based Decision Support System (DSS) to combine machine learning forecasting, spatial analytics, and optimization modeling to improve the efficiency of teacher demand prediction and allocation. The suggested framework will use historical data on enrollments as well as demographic data, records of teacher attrition, and geospatial data to project future staffing needs. Random Forest and Gradient Boosting models are compared in terms of forecasting performance and geographic differences are defined by spatial clustering and hotspots analysis. The optimization model of an integer linear programming minimizes the cost of staffing imbalance and allocation with regulatory and budget constraints. The results of the experiment indicate higher accuracy in prediction (R^2 = 0.92) and 27% lowering of the staffing imbalance than the standard allocation procedures. The suggested system offers a data-driven and spatially intelligent planning tool of strategic educational workforce planning in the hands of policymakers.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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creator: maherali1976 Ahmed76
creator: Nisreen Mustafa Sajid
rights: الحقوق الفكرية (c) 2026 maherali1976 Ahmed76, Nisreen Mustafa Sajid (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 29
ending page: 46
doi: 10.61710/e1f03589

Chicken Swarm Optimization based QoS Web Service Composition in Cloud Computing Environment
type: item
description: The increasing volume of data in the cloud computing environment has led to a situation where a single, abstract cloud web service has lost its ability to meet the complex requirements of various customers. Therefore, a service composition is becoming a necessity in the cloud computing environment. In this paper, Chicken Swarm Optimization (CSO) based QoS Web Service Composition in Cloud Computing Environment CSOQSC_CCE algorithm has been proposed. CSO is a metaheuristic algorithm that inspired from the hierarchy and foraging habits of the chicken swarm. The proposed CSOQSC_CCE algorithm is validated through using the Quality of Web Service (QWS) which is a real-world service dataset. The investigation results show that the proposed CSOQSC_CCE algorithm overcome the existing approaches in terms of response time and delay.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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creator: Dina Alshibani
rights: الحقوق الفكرية (c) 2026 Dina Alshibani (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 37
ending page: 45
doi: 10.61710/kjcs.v4i1.153

تحليل تدهور القرار الذكي في ظل التوسع الشديد للبيانات في نظم معلومات المؤسسات
type: item
description: This paper provides a computer framework describing a research on the deterioration of the decision making of AI based enterprise information systems in reaction to the extreme data scale issues. The pattern of erosion of the decision-making accuracy of four AI architectures, that is, neural networks, random forests, support vector machines, and ensemble approaches, was studied by means of controlled multi-scenario simulation. These experiments were performed in four cases of scalability of linear growth, exponential burst, step-wise expansion and random volatility. One of the most important innovations in our approach is a simulation engine which will be employed in order to measure degradation using various performance measures i.e. decision accuracy, response time, uniformity and computational load. We marked important threshold limits in our analysis, where the reliability of the system will be very low and where an early warning of the looming collapse would be detected. We also came up with predictive models that we used to predict degradation patterns. The results show that the ensemble methods are much stronger with the average accuracy of 94.3 percent even in extreme stress conditions, which are not present in traditional architectures. On the other hand, exponential bursts exhibit the greatest performance disparities, and after their thresholds are exceeded, at least 25 performances are required to initiate execution. Finally, this work provides new techniques for assessing recovery dynamics, resilience, and decay rates. We provide organizations that are having trouble simplifying their architectures with practical, hands-on guidance. The most important lessons are to maintain decision-making for workloads involving a lot of data by offering proactive system layout policies. The established threshold detection and preemptive intervention techniques are part of a new paradigm of predictive system management that we have developed.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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creator: Shaymaa kaseb Layus
creator: صالح حاجم جلود
creator: جمال محسن الركابي
creator: محمد كاسب لعيوس الحسناوي
rights: الحقوق الفكرية (c) 2026 Shaymaa kaseb Layus, Salih Hajem Glood , Jamal M. Alrikabi, Mohammad Kaisb Layous Alhasnawi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 55
ending page: 77
doi: 10.61710/kjcs.v4i1.155

علوم الحاسوب تحليلات الأداء المدعومة بالذكاء الاصطناعي للعاملين في مجال التعليم: دراسة تجريبية في المدارس العراقية
type: item
description: تُركز هذه الأخيرة على البحث في تساهم في تحليلات الأداء التقرير في الذكاء الاصطناعي على الكادر التعليمي في المدارس العراقية، وتساعد على جودة التدريس، وإدارة الإدارة، وأداء المؤسسة التعليمية. أبرزت الدراسة إلى مراجعة القصور في إجراءات التقييم التقليدي، التي تعتمد الغالبية على أداء الطلاب في الامتحانات، وتقديم جزئية وبطيئة حول كفاءة التدريس. تم إعداد استبيان مُهيكل لتمثيل تمثيلية من حيث المبدأ والإداريين، ثم تحليل البيانات باستخدام برنامج SPSS الإصدار 25، من خلال السيولة الموثوقة (معامل ألفا لكرون باخ)، واختبارات الربح (معامل ارتباط بيرسون)، والإحصاء الوصفي، وتحليل الإحالة (البسيط والمتعدد) لتفسيرات الدراسة. هناك ارتباط إيجابي قوي بين تطبيق نظام دعم الابتكارات في الذكاء الاصطناعي، وذلك لأنه يساهم في التخطيط المستمر، وتحسين الابتكارات التكنولوجية. وتمثل النقاط الرئيسية الاتجاهات في تطوير دمج الذكاء الاصطناعي في المدارس، وتدريب الكادر التعليمي، تحليلات النحاس الآن لتحسين الموارد وتيسير اتخاذ القرار في الأدلة. يمكن أن تساعد في دعم النتائج المتاحة لصانعي النتائج وتحسين النتائج بشكل كبير بمساعدة التحليلات الذكية.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
  topic: keywords
subject: Array
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creator: maherali1976 Ahmed76
rights: الحقوق الفكرية (c) 2026 maherali1976 Ahmed76 (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 88
ending page: 103
doi: 10.61710/kjcs.v4i1.161

دمج الصور متعددة البؤر باستخدام خوارزمية جينية، مع مراعاة تباين شدة الإضاءة المحلية وخصائص الحواف.
type: item
description: تقترح هذه الدراسة منهجًا لدمج الصور متعددة التركيز، يعتمد على تحسين الخوارزمية الجينية لاختيار أوزان الدمج الأنسب، والتي تُستخدم بدورها لرفع جودة الصورة المدمجة الناتجة. يتم استخدام مرشح الانحراف المعياري لاستخراج خصائص النسيج، وتحديد كشف الحواف باستخدام مُعامل سوبل، بالإضافة إلى تحديد خاصية التباين. تُستخدم الأوزان المُحسّنة لتحديد أفضل توليفة من خرائط خصائص هذه الخصائص الثلاث المستخرجة، وذلك لتحقيق عملية دمج دقيقة. تُظهر التجارب أن هذا المنهج يحافظ بنجاح على النسيج والحواف والتباين، مما ينتج عنه صورة مدمجة ذات جودة بصرية محسّنة وغنية بالمعلومات.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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subject: Array
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creator: Muthana Mahdi
rights: الحقوق الفكرية (c) 2026 Muthana Mahdi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 46
ending page: 54
doi: 10.61710/kjcs.v4i1.154

Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP
type: item
description: يُقدّم هذا العمل مفهوم "الانحراف العاطفي" في الترجمة الأدبية، والذي يُشير إلى انحراف أو تحوّل الإشارات العاطفية من النص الأصلي إلى ترجماته. ويركّز هذا البحث على تحليل ثنائي اللغة للترجمتين العربية والإنجليزية لملحمة جلجامش، ويهدف، من خلال الاستفادة من التطورات الحديثة في معالجة اللغة الطبيعية (NLP) مع التركيز على تحليل المشاعر والتعرّف على العواطف، إلى قياس المحتوى العاطفي. وتتمثّل الأهداف النهائية لهذا البحث في تطوير منهجية حاسوبية لاستخراج المحتوى العاطفي من مجموعات النصوص الأدبية ثنائية اللغة ومقارنتها، وقياس الانحراف العاطفي عبر نسخ مختلفة من ملحمة جلجامش، ووضع هذه النتائج في سياق أوسع لأبحاث الترجمة. ويُتيح التقاء اللغويات الحاسوبية ونظرية الترجمة والتحليل الأدبي الرقمي آفاقًا جديدة للدراسات الأدبية المدعومة بالذكاء الاصطناعي، ويُوفّر إطارًا قابلًا للتكرار لدراسة مسألة الدقة العاطفية في الأدب المترجم.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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subject: Array
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creator: Zaid Al-Araji
creator: Balqees Talal Hasan
rights: الحقوق الفكرية (c) 2026 Zaid Al-Araji (Author); Balqees Talal Hasan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 12
ending page: 21
doi: 10.61710/kjcs.v4i1.146

A Hybrid CNN–ViT Deep Learning Framework for Detecting and Classifying Image Manipulation in Online News Media
type: item
description: The tremendous growth in the flow of information online, and its quick evolution in the digital age has led to serious challenges. Fake news, particularly that which relies on dubious or manipulated images (such as using Adobe Photoshop or GAN networks), deepfake techniques, has become a clear danger to credibility and public opinion are among the most dangerous practice of media manipulation, because to their ability to directly influence the public and weaken trust in media sources and news organizations .The purpose of this work was to develop a hybrid system model for detecting misleading and manipulated images on online news websites. This was through an integrated model based on the Vision transducer (ViT) combined a model that integrates with convolutional neural networks (ResNet50 and EfficientNetB3).The suggested model leverages the capabilities of convolutional the networks for feature extraction and the analysis of long-range relevance inside an image, utilizing the capabilities of the Vision Transducer. This provides for more accurate visual portrayal. The model was developed using pre-trained weights and translational learning, and then father enhanced by adding leakage layers to reduce over-allocation. The proposed model was training and achieved a test accuracy of 78.2%. The stability of performance during the verification phase confirms the model’s effectiveness and reliability in verifying the authenticity of images in digital media before publication.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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creator: marwa raid
creator: Raghad Majeed Azawi
rights: الحقوق الفكرية (c) 2026 marwa raid (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 1
ending page: 15
doi: 10.61710/wkdtnn58

Twitter Bot Detection Using Relational Graph Convolutional Networks and Convolutional Neural Networks
type: item
description: تزايد أعداد الحسابات الوهمية والبوتات على تويتر يشكّل تهديدًا للأمن السيبراني، وسلامة المعلومات، والنقاش العام. تعتمد الأساليب الحديثة لاكتشاف البوتات وحظرها بشكل أساسي على البيانات الوصفية لمعلومات المستخدم، والمحتوى، والخصائص السلوكية (heuristics)، إلا أنها أثبتت عدم فعاليتها أمام البوتات الأكثر تطورًا التي تحاكي سلوك المستخدم الحقيقي. يمكن أن تساعد الشبكات العصبية الرسومية (Graph Neural Networks - GNNs) في هذا المجال. إذ يمكن نمذجة تفاعلات المستخدمين ووسائل التواصل الاجتماعي على شكل رسم بياني (Graph)، حيث يُمثَّل المستخدمون كعُقد (Nodes) وتمثل تفاعلاتهم كروابط (Edges)، وتستطيع شبكات GNN دمج البيانات الفردية والعلائقية في كيان واحد متكامل. تقوم هذه الورقة بتحليل هذه الحسابات من منظورين منهجيين مختلفين، هما: الشبكات العصبية الالتفافية التقليدية (CNNs) وشبكات الالتفاف البياني العلائقي (Relational Graph Convolutional Networks - R-GCNs). ورغم أن نماذج CNN أظهرت كفاءة عالية في التعرف على المؤشرات اللغوية والبيانات الوصفية، فإن اعتمادها على الخصائص السطحية حدَّ من قدرتها على التعامل مع النصوص والبيانات المصممة لمحاكاة السلوك البشري بدقة. أما بنية نموذج R-GCN فقد كانت الأكثر تميزًا، حيث تفوقت على جميع النماذج، لا سيما من حيث مقاييس F1 وROC-AUC، وذلك بفضل قدرتها على التقاط أنماط العلاقات المختلفة مثل المتابعة (Follow)، وإعادة التغريد (Retweet)، والإشارة (Mention). يوضح هذا العمل إمكانات وحدود أنظمة الكشف الحالية، ويقترح مسارات واعدة مستقبلية تشمل التفسير الآلي، ودمج الوسائط المتعددة، والتكيّف عبر المنصات المختلفة.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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subject: Array
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creator: Hind Mohammed
rights: الحقوق الفكرية (c) 2026 Hind Mohammed (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 16
ending page: 28
doi: 10.61710/jfmaxm44

A Hybrid AI Model for Early Detection of Heart Disease Using Clinical and ECG Data
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description: One of the main causes of morbidity and death is still cardiovascular diseases (CVDs) thereby stressing the need for developing effective early diagnostic tools. This paper proposes a hybrid approach using AI that combines deep ECG feature learning with clinical data to enhance early heart disease diagnosis. The ECG signals were preprocessed, divided into 2-second epochs, and then represented as 128-dimensional embedding using a 1D CNN-based feature extractor. At the same time, a set of 13 clinical risk factors was normalized and used to construct a lightweight clinical prediction branch. Both were then fused using a multimodal neural network that learns both ECG and clinical features. The findings of the experiment have demonstrated the effectiveness of the proposed hybrid framework, which has achieved 97.56% accuracy, 0.996 AUC, 99.0% sensitivity, and 96.0% specificity, significantly outperforming the individual models. Furthermore, the SHAP values-based explain ability method has assisted in revealing the most important clinical variables and ECG embedding features, thereby making the prediction results interpretable and meaningful from the clinical perspective. In summary, the proposed approach has shown how multimodal AI systems might help with early heart disease diagnosis and offers a promising solution for future real-time applications.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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creator: Hayder Ali Kadhim
rights: الحقوق الفكرية (c) 2026 Hayder Ali Kadhim (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 47
ending page: 57
doi: 10.61710/5h88yc19

Experimental Evaluation of Adaptive Multi-Agent AI Models for Detecting Stealthy Cyber Attacks in Dynamic Network Environments based on MADDPG
type: item
description: يواجه الأمن السيبراني تحديات متزايدة نتيجة للهجمات المتطورة والسرية التي تستهدف الشبكات الديناميكية. يهدف هذا البحث إلى استكشاف فعالية نماذج الذكاء الاصطناعي متعددة العوامل القابلة للتكيف في كشف هذه الهجمات باستخدام خوارزمية MADDPG (خوارزمية تدرج السياسة الحتمية العميقة متعددة العوامل). وقد صُممت بيئة شبكة ديناميكية لمحاكاة حركة البيانات الطبيعية والهجمات السيبرانية السرية، حيث يراقب كل عامل جزءًا محددًا من الشبكة ويتخذ قرارات فورية لكشف الهجمات أو التخفيف من آثارها.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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creator: asma ibrahem
rights: الحقوق الفكرية (c) 2026 asma ibrahem (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 78
ending page: 87
doi: 10.61710/kjcs.v4i1.157

Developing an Enhanced IHBO Algorithm with Chaotic Binary Hashing to Optimize BiGRU for Industry 4.0 IIoT Anomaly Detection
type: item
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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subject: Array
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creator: SAIF SAAD ALAMSHANI
rights: الحقوق الفكرية (c) 2026 SAIF SAAD ALAMSHANI (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 1
ending page: 11
doi: 10.61710/kjcs.v4i1.138

Federated Learning for Early Detection of Advanced Persistent Threats in IoT Networks
type: item
description: In the era of connected IoT devices, ensuring cybersecurity while preserving data privacy is increasingly critical. Federated learning offers a promising approach by enabling collaborative training of detection systems without sharing raw data. This paper presents a novel federated Intrusion Detection System (IDS) based on XGBoost algorithm, and for the first time designed to detect initial compromise (I.C.) phase of Advanced Persistent Threats (APTs) in distributed Internet of Things (IoT) environments. By leveraging the federated framework, the IDS achieves robust detection across multiple devices while maintaining privacy and minimizing computational overhead. Extensive simulation results indicated that our proposed method achieved a precision of 97%, recall of 100%, and F1-score of 98%, providing a practical and efficient solution for real-world IoT security challenges.
subject: Computer Science
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subject: علوم الحاسوب
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creator: bassam noori
rights: الحقوق الفكرية (c) 2025 bassam noori (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 100
ending page: 108
doi: 10.61710/kjcs.v3i4.140

Practical Implementation and Analysis of Software Metrics Impact on Maintainability in Open-Source Systems
type: item
description: Common software metrics and maintainability measures in open-source Java are examined in this research.The authors test the major object-oriented metrics—Coupling Between Objects (CBO), Lines of Code (LOC), Weighted Methods per Class (WMC), Lack of Cohesion of Methods (LCOM), Depth of Inheritance Tree (DIT), and Cyclomatic Complexity—against real-world maintainability indicators like bug counts, code modifications, and developer turnover. The authors use Python data analysis and visualization to find statistically significant patterns in Spearman correlation analysis. The findings indicate that metrics such as CBO and cyclomatic complexity are very predictive in terms of maintenance effort, whereas others, such as DIT, provide little insight. In addition to theoretical justification, this work provides a practical, reproducible workflow to be implemented by software engineers to give priority to code quality and make more intelligent maintenance decisions. Finally, this study ties the strengths of academic measures and the real-world aspects of daily software engineering.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
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creator: Nadia Hussien
rights: الحقوق الفكرية (c) 2025 Nadia Hussien (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 87
ending page: 99
doi: 10.61710/kjcs.v3i4.135

Explainable Artificial Intelligence Integrated Ensemble Learning Framework for Diabetes Prediction
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description: Accurate prediction of diabetes based on clinical and demographic indicators is essential, as early prediction of this chronic metabolic disorder plays a critical role in preventing long-term organ complications. However, existing research continues to face significant challenges, including pronounced class imbalance, the scarcity of large and diverse datasets, and limited integration of explainable artificial intelligence. This research compares several ensemble learning methods (Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Histogram based Gradient Boosting, extremely randomized Trees, and XGBoost) on a large imbalanced dataset (87,664 negative vs. 8,482 positive samples). To mitigate imbalance, we evaluate six resampling approaches including (Random Over Sampling, Random Under Sampling, Synthetic Minority Over-sampling, Adaptive Synthetic Sampling, Tomek Links Removal Sampling and (Synthetic Minority Over-sampling with Edited Nearest Neighbors). We assess models using metrics robust to class imbalance (precision, recall, F1, AUC-ROC, and AUC-PR) and calibration measures. The Extra Trees classifier achieved the highest measured accuracy (0.994); with Random Over Sampling for balancing dataset. also, these results were compared with several previous works and number of machine learning algorithms, and the results showed superiority. Explainability is performed at both global and local levels: permutation and SHAP for global feature importance, and (Local Interpretable Model-Agnostic Explanations) force plots for instance-level reasoning. however, we analyzed this result using sensitivity, specificity, PR-AUC and calibration, we report detailed experiments showing how resampling method, hyperparameter tuning, and stratified validation influence performance. Finally, we provide clinical-relevant insights from SHAP analyses and discuss limitations and future directions for deploying interpretable models in screening workflows.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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creator: Mohammed Mohammed Abdallazez
rights: الحقوق الفكرية (c) 2025 Mohammed Mohammed Abdallazez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 109
ending page: 126
doi: 10.61710/kjcs.v3i4.141

Optimized Hybrid CNN-LSTM Framework with Multi-Feature Analysis and SMOTE for Intrusion Detection in SDN
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description: There is a growing trend toward the use of software-defined networks (SDN), which presents new security challenges requiring advanced intrusion detection systems (IDS). This paper proposes a deep learning-based hybrid system combining convolutional neural networks (CNNs) and long-term short-term memory networks (LSTMs) that can be used for effective intrusion detection in SDN environments. The model uses CNNs to acquire the spatial features of network traffic data and LSTMs to learn temporal patterns, enabling the identification of complex attack patterns. We evaluate our model using an InSDN dataset and test its performance using various feature sets, ranging from 6 to 83 features in our model. Experimental results indicate that our model has a high multi-class classification accuracy of 99.63% when using all 83 features in Group1. Furthermore, we use a Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance which considerably enhances detection accuracy of minority attack classes which is reaching 99.76%. It is established that the presented hybrid CNN-LSTM model is powerful and successful solution to SDN security improvement.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Dalia Shihab
creator: Abbas Abdulazeez Abdulhameed
creator: Methaq T. Gaata Gaata
rights: الحقوق الفكرية (c) 2025 Dalia Shihab, Abbas Abdulazeez Abdulhameed, Methaq T. Gaata (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 55
ending page: 75
doi: 10.61710/kjcs.v3i4.132

Computationally Efficient Hybrid Framework for MNIST Handwritten Digit Recognition
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description: This research work suggests a combination classification technique for handwritten digit recognition based on MNIST dataset. The design embraces Principal Component Analysis (PCA) to reduce the level of dimensionality, KMeans clustering to get the pattern of the framework, and Random Forest to make the final prediction. By compressing the feature space into a 50 dimensional space and adding in cluster information, we are able to attain an accuracy of about 93.1 percent. The significant contributions of this paper are to prove the PCA-KMeans integration useful to improve classification, offering a computationally efficient solution as alternative to deep learning model, and to prove that feature augmentation using clustering outcomes leads to better discrimination of visually similar digits. The data suggest that the referred to hybrid model is stable and feasible approach to image identification and pattern recognition, which could be applied to more complicated dataset.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Dalia Shihab
rights: الحقوق الفكرية (c) 2025 Dalia Shihab (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 76
ending page: 86
doi: 10.61710/kjcs.v3i4.134

The Connected Classroom: Leveraging EdTech to Enhance Student Engagement
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description: تناولت هذه الدراسة متعددة الأساليب تصورات طلاب الجامعات لدور التكنولوجيا في تعزيز المشاركة والتعاون والاستقلالية. جُمعت البيانات من 120 طالبًا (65 طالبة و55 ميلاً) من ثلاث جامعات (الجامعة المستنصرية، وجامعة ديالى، وجامعة واسط) من خلال المشاركة في الاستبيانات وملاحظات الفصول الدراسية والتغذية الراجعة المفتوحة. أظهرت النتائج أن الطلاب لديهم وجهات نظر إيجابية للغاية، حيث كانت المشاركة (م = 4.12) هي الميزة الأكثر ملاحظة، تليها التعاون (م = 3.98) والاستقلالية (م = 3.85). كشف التحليل الاستدلالي عن اختلافات كبيرة في تصورات التعاون: أبلغت الطالبات عن درجات أعلى من الطلاب (ت (118) = 2.04، ص = 0.04)، وأبلغ طلاب جامعة المستنصرية عن تعاون أعلى بكثير من طلاب جامعة أخرى (ف (2,117) = 3.67، ص = 0.028). لم يتم اكتشاف أي اختلافات كبيرة في المشاركة أو الاستقلالية. كشفت بيانات الرصد عن مستويات عالية من التعاون بين الأقران، بينما سلّطت الملاحظات النوعية الضوء على الفرص الرئيسية (مثل مرونة التعلم) والتحديات (مثل مشاكل التواصل، وفجوات المعرفة الرقمية). وتشير النتائج إلى أنه على الرغم من فعالية التكنولوجيا في إشراك الطلاب، إلا أن قدرتها على تعزيز التعاون تعتمد على عوامل ديموغرافية ومؤسسية. وتؤكد الدراسة على أهمية التصميم التربوي المدروس والدعم المؤسسي القوي للاستفادة من إمكانات التكنولوجيا التعاونية بكفاءة وعدالة.
subject: Computer Science
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creator: جابر محمد الدليمي
rights: الحقوق الفكرية (c) 2025 Jaber (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 37
ending page: 54
doi: 10.61710/kjcs.v3i4.123

Deep Learning-Based Blood Cell Classification with Enhanced Data Preprocessing and Augmentation
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description: Classification of blood cells accurately is an extremely important task in the field of hematology, for the diagnosis of blood disorders and for guiding decision making in the clinical context. In this paper, we use a high-quality dataset of 17,092 microscopic peripheral blood cell images from the Hospital Clinic of Barcelona, encompassing eight different cell types, all of which have been annotated by expert pathologists. To improve model performance and to tackle the class imbalance in the dataset, we developed a strong data preprocessing and data augmentation pipeline which includes contrast enhancement, normalization, geometric and photometric transformations, injection of noise, and mixup style synthetic data. We develop two state-of-the-art deep learning models (EfficientNet-B0, ResNet50) to enable benchmarking of the proposed pipeline. In our experimental results, EfficientNet-B0 achieved overall accuracy of approximately 98.3% and ResNet50 achieved accuracy of 98.6%, with very good precision, recall, and F1-scores for all classes. These preliminary results demonstrate the effectiveness of the designed data preprocessing and data augmentation strategies, as well as provide a benchmark for managing blood cell images in hematology for future research.
subject: Computer Science
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creator: marwa raid
rights: الحقوق الفكرية (c) 2025 marwa raid (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 22
ending page: 36
doi: 10.61710/kjcs.v3i4.122

Cognitive Honeypots AI-Enhanced Deception for Proactive Threat Hunting
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description: The rapidly increasing complexity of cyber threats, including AI-powered attacks, is forcing a shift in defense strategies from reactive to proactive approaches. Traditional honeypots remain largely static and easily identifiable, while newer AI-enhanced models emphasize realism but still lack deep understanding of attacker cognition. To address this gap, this paper introduces CogniTrap, a novel framework that combines a high-interaction honeypot with an AI-driven cognitive deception engine. CogniTrap dynamically creates and adapts “cognitive decoys” designed to exploit attackers’ biases and reasoning flaws. A prototype of CogniTrap was developed and deployed, where decoy placements and adaptations were optimized using reinforcement learning informed by live analysis of attacker tactics, techniques, and procedures (TTPs). Intelligence gathered from triggered decoys was transformed into proactive hypotheses for threat hunting in production environments. Experimental results from comparative 30-day live deployments showed that CogniTrap increased attacker dwell time by 45% compared to a standard high-interaction honeypot and generated higher interaction rates with deceptive assets. Furthermore, it was able to produce high-fidelity threat hunting queries based on attacker cognitive patterns, validating its practical utility. This research marks the first implementation-based framework for adaptive cognitive honeypots, bridging the gap between theoretical cognitive security concepts and operational proactive threat hunting. By providing architecture, algorithms, and empirical validation, CogniTrap establishes a new paradigm for intelligent cyber defense.
subject: Computer Science
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creator: بارق ماهر
creator: كرار ماهر
creator: رؤى رياض
rights: الحقوق الفكرية (c) 2025 bareq maher, Karrar M. Khudhair, Ruaa Riyadh hadi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 55
ending page: 70
doi: 10.61710/kjcs.v3i3.121

Analyzing Big Data to Mitigate Cyber Attacks Using Machine Learning Classifications: A Comparative Study of an Efficient Classifier Set
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description: In this research, we address a set of methodological approaches and approaches in scientific research in the field of cybersecurity and big data analysis using machine learning techniques, which have made the data analysis process easier to understand and mitigate cyberattacks. This method involves data processing and analysis in a series of stages, or in the form of analytical protection layers, including analysis and organization of large databases,. Training and qualifying classifiers is a method for reducing the dimensionality and skewness of feature vectors according to basic procedure analysis.. Using the principal components in the analysis, we employ various binary classifiers for vector K-nearest neighbors and many other algorithms, such as Bayesian algorithms and others, analyze vector machines, as well as artificial intelligence, which work to increase the efficiency and accuracy of attack detection devices. Artificial neural networks are also highly efficient in detecting cyberattacks and analyzing all network disturbances. Through our research, the basic idea was to classify and segment data and deal with each type of data separately to facilitate processing. Another approach is to combine data classifiers. This idea relies on two options: soft voting and majority voting. Each approach has its own method and method for specific use. methods for intrusion detection and penetration of large databases. In the first approach, we analyze and process data in parallel by classifying and dividing it into several subsets, and assigning each subset a separate path. In this method, we partition the problem, making the solution simpler and the data less complex. In the other approach, we also use sensors to collect client data within search servers. The sensor contains a set of parallel paths, each path analyzes the data and information for the client, and is conducted through a parallel network that detects anomalies through two different data sets. One group cont…
subject: Computer Science
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subject: علوم الحاسوب
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creator: ali jawad
rights: الحقوق الفكرية (c) 2025 Ali Jawad (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 1
ending page: 21
doi: 10.61710/kjcs.v3i4.113

Multi-Dimensional Deep Learning for Unusual Detection in Security Footage: A 3D CNN, LSTM, and Attention-Based Approach
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description: لا يزال الكشف الآلي عن الأنشطة غير الاعتيادية في فيديوهات المراقبة يُمثل تحديًا بالغ الأهمية نظرًا للكم الهائل من اللقطات وندرة الأحداث الشاذة. تقترح هذه الدراسة إطار عمل مبتكرًا للتعلم العميق يدمج الشبكات العصبية التلافيفية ثلاثية الأبعاد لاستخراج السمات المكانية الزمنية، وشبكات الذاكرة طويلة المدى قصيرة المدى لنمذجة التبعيات الزمنية، وآلية انتباه للتركيز على الأجزاء البارزة. الهدف الرئيسي هو تحقيق تصنيف ثنائي دقيق للغاية لمقاطع الفيديو إلى فئتين "معتادة" و"غير عادية"، مع معالجة اختلال التوازن بين الفئات والتباين البيئي. تم تدريب النموذج وتقييمه على ثلاث مجموعات بيانات واسعة النطاق، وهي UCF-Crime وXD-Violence وCCTVFights، والتي تتضمن فيديوهات مراقبة تغطي شذوذًا في العالم الحقيقي. تُظهر النتائج التجريبية أن الطريقة المقترحة تُحقق دقة إجمالية تبلغ 97.41% في مجموعة بيانات UCF-Crime، و98.11% في مجموعة بيانات XD-Violence، و98.50% في مجموعة بيانات CCTVFights، بالإضافة إلى دقة عالية، ودرجات تذكر، وF1 في مجموعات البيانات الثلاث المستخدمة في عملية التقييم، متفوقةً بذلك على المعايير الحالية. تشير هذه النتائج إلى أن الجمع بين النمذجة المكانية الزمنية وتجميع السياقات القائمة على الانتباه يُمكن أن يُحسّن بشكل كبير من أداء اكتشاف الشذوذ في سيناريوهات المراقبة المعقدة.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Nadia Ali
rights: الحقوق الفكرية (c) 2025 Nadia Ali (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 1
ending page: 15
doi: 10.61710/kjcs.v3i3.114

Pepper Leaf Disease Detection Using Deep Learning Techniques
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description: Early diagnosis of pests and plant diseases is crucial for preventing significant crop losses. This study proposes a leaf disease detection system using MobileNetV2 integrated with multiple optimization techniques (Adam and learning rate scheduling). Evaluated on the Pepper PlantVillage dataset, the MobileNetV2 model employs patch embedding and attention mechanisms for feature extraction, with SoftMax used for final classification. The model was further validated on a multi-class Apple PlantVillage dataset. Results demonstrate high accuracy: 97.03% for pepper and 94.63% for apple classification. Comparative analysis with CNN architectures shows superior efficiency and faster convergence for our model, outperforming Inception v3 (96.81%) and VGG-19 (94.93%) on the pepper dataset.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Lafta Raheem Ali Al-khazraji
rights: الحقوق الفكرية (c) 2025 Lafta Raheem Ali Al-khazraji (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 45
ending page: 54
doi: 10.61710/kjcs.v3i3.118