I am a Ph.D. student in the computer science department at the University of Alabama. I am a member of Dr. Xiao's lab (W4Net). I am lecturing at the King Khalid University, Saudi Arabia.
Research Interst
Phishing detection
Advertisement attacks
Natural Language Processing
Cybercrime
News
I received the Computer Science Department's Outstanding Graduate research award for the 2024 academic year.
One paper was accepted Computer & Security Elsevier Journal 2024
I passed my Ph.D. defense on March,5 2024. I am now a Ph.D. in Computer Science.
One paper was accepted in ACMSE 2024 conference
I joined the 2024 ACM Southeast Conference (ACMSE 2024) as a committee program member
In December 2023, I was awarded to attend the Neural Information Processing Systems (NeurIPS) conference in New Orleans, Louisiana, USA
One paper was published in MDPI Electronics journal 2023
I passed my Ph.D. proposal exam on Apr,20 2023. I am now a Ph.D. candidate in Computer Science.
One paper was published in ACMSE 2023 conference
One paper was published in IEEE Access Journal 2023
One paper was published in ACMSE 2022 conference
One paper was published in MDPI Electronics Journal 2022
I passed my Ph.D. qualifying exam on Sep,12 2021.
One paper was published in the 27th ACM SIGKDD Conference 2021
Resume:
Education
Bachelor of Computer Science
2014
King Khalid University, KSA
Master of Computer and Information Science
2029
Gannon University, USA
Philosophy of Computer Science
2024
The University of Alabama, USA
Experience
Web developer
2014 - 2016
Private Company, KSA
Lecturer
2016 - Present
King Khalid University, KSA
Member of The High Performance Data Analytics and Computing (HiPDAC) Lab
Phishing attacks are a type of cybercrime that use social engineering to trick users into revealing sensitive information, such as personal identity or financial data. Attackers can design fake web pages to mimic legitimate sites, or send fraudulent emails or social media messages that appear to come from a trusted source. The rise of social media has made it easier for attackers to reach a larger pool of potential victims. According to the Anti-Phishing Working Group (APWG), there were over 250,000 phishing attacks reported in January 2021, a record high. Business compromise attacks also increased by 56% from the last quarter of 2020 to the first quarter of 2021. Financial institutions and social media sites are the most commonly targeted websites. Phishing attacks can have a devastating impact on victims. Attackers can use stolen information to commit identity theft, make unauthorized purchases, or even take control of victims' financial accounts. Phishing attacks can also damage businesses by disrupting operations, stealing customer data, or causing repetitional damage. Our goal is to develop a real-time phishing detection system to protect users from these attacks. Our system will use a variety of techniques, including machine learning and natural language processing, to identify and block phishing emails, websites, and social media messages.
Date: Present
PhD Research
Advisor: Dr. Yang Xiao
The University of Alabama
Location Extraction from Social Media Posts
In 2017 Hurricane Harvey ruined multiple areas in Houston, Texas. Many homes were destroyed, or the flood prison people in their homes. Therefore, people started to contact the rescue to save them. However,due to the large number of people who needed saving, the call center was busy. Therefore, the people started to use social media to announce their needs for help so the volunteer organization could identify their location to rescue them. So, using Natural language processing to identify the location of people who needs helps artificially using social media post. Our goal was to design a model to analyze the social media tweets and extract the location.
Date: 2020
PhD Research
Advisor: Dr. Zhe Jiang
The University of Alabama
Bird Species Detection
Computer vision tasks have recently grown and become one of the most challenging tasks in deep learning. One of these tasks is instance segmentation, which is considered a difficult task in computer vision because of the ability to generate different mask colors on each object in the image. Mask-RCNN Proposed using three networks such as Region Proposed network to locate the object and three CNN networks, one for classification, one for extract feature, and another for generating the mask. These networks together make Mask R-CNN robust because it makes the image pass to different steps; each step got more in-depth in the image. Mask R-CNN showed an excellent result with the COCO dataset. So, we trained it on a fine-grained classification dataset such as birds’ species. It is a very challenging dataset because it contains several species, that each one of these species has very similar details from another. We trained Mask R-CNN on 100 species from CUB-200-2011 dataset