Dr. Abeer Tariq Maolood
PROFESSOR
Prof. Dr. Abeer Tariq Maolood received theMSc. and PhD. in Computer Science from University of Technology, Iraq, 2005 and 2010 respectively. She has around 15 years of teaching experience. Her areas of interest’s computer and network security, neural networks and web applications security.
Education
- BSc.-Computer science- University of Technology-1997/1998
- MSc.-Computer science- University of Technology-2005/2006
- PhD.- Computer and Data Security- University of Technology-2009/2010
Research Interests
- Cyber security
- Neural Networks /Deep Learning
- Ethical Hacking-Web Apps. And Web Apps. Security
- Network Security / Cloud Computing security
Teaching
- Adaptive Algorithms/5 Years University of Technology
- Compiler Design/10 years University of Technology
- Computer and Data Security/1 year University of Technology
- Computation Theory/4 year University of Technology
- Adaptive Algorithms/5 Years University of Technology
- Compiler Design/10 years University of Technology
- Computer and Data Security/1 year University of Technology
- Computation Theory/4 year University of Technology
- Ethical Hacking/1 year University of Technology
- E-Techniques/MSc./5 Years University of Technology
- Intrusion Detection/MSc./1 Year University of Technology
- IT Security/MSc. 1 Year University of Technology
- Cloud Cryptography/1 Year University of Technology
- Business Process and development systems/2 Years University of Technology
Most recent publications
- E-commerce: Security Enhancement In Internet Banking
- Improving Diffie-Hellman Key Exchange Using Irreducible Polynomials
- Propose System for Sound Retrieval Using MAS and ANN
- A new approach for hiding data within executable computer program files using an improvement cover
- Improving Laboratories Efficiency through Website Using Text Mining
- SMSCC: Smarter and More Secure Credit Card Using Neural Networks in Zero Knowledge protocol
- PROPOSAL TO ENHANCE FINGERPRINT RECOGNITION SYSTEM
- Enhance Criminal Investigation by Proposed Fingerprint Recognition System
- The Future for Adaptive Software Development in Cloud Computing Environment Using Multi Agent System
- MSOM: Modified Self Organizing Map for Faster Winning Cluster Detection
- Improve Image Retrieval using Modified Fuzzy color& texture Histogram
- Propose more efficient e- commerce website for searching and purchasing
- Modifying Advanced Encryption Standard (AES) Algorithm
- “ Signature-based and Supervised Learning to Improve Data Loss Protection ”
- E-commerce Transactions of Behaviors Anomaly Detection System
- An Effective Preprocessing Step Algorithm in Text Mining Application
- Best approximate of vector space model by using SVD
- A Fuzzy approach based for documents datasets clustering
- “Proposed Method to Enhance Text Document Clustering Using Improved Fuzzy C Mean Algorithm with Named Entity Tag “
- Performance Evaluation of the Electromagnetic Behavior of the Bundle SWCNTs with Circular Geometry
- Enhancing supervised detection using decision tree and decision table
- Proposed Data Loss Protection in Electronic health record
- User behaviors attributes of database anomaly detection model
- Towards Generating Robust Key Based on Neural Networks and Chaos Theory
- Towards Generating a New Strong key for AES Encryption Method Depending on 2D Henon Map
- Network Anomaly Detection Using Unsupervised Machine Learning: Comparative Study
- Entropy analysis and image encryption application based on a new chaotic system crossing a cylinder
- Determining of Robust Factors for Detecting IoT Attacks
- Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning
- Design the Modified Multi Practical Swarm Optimization to Enhance Fraud Detection
- Machine Learning-Based Detection of Credit Card Fraud: A Comparative Study
- Banking Intrusion Detection Systems based on customers behavior using Machine Learning algorithms: Comprehensive study
- Improve The Support Vector Machine Using Modified Practical Swarm Optimization to Enhance Fraud Detection
- Credit card Anomaly detection using improved deep Auto encoder algorithm
- Women’s day award 2017 / University of Technology
- Award in 2nd computer science conference, 2019