Abeer Aljohani
Papers
1
Total Citations
4
H-Index
1
About
Abeer Aljohani is a researcher at the forefront of cybersecurity and machine learning, with a particular focus on safeguarding robotic systems and critical infrastructures. Her work addresses the growing vulnerability of interconnected devices in industries such as manufacturing, healthcare, and logistics. Aljohani’s most cited paper, "A novel univariate feature selection with ANOVA F-test-based machine learning model for Intrusion Detection Framework of Robotics system" (2025), introduces an innovative approach that combines statistical feature selection with machine learning to enhance intrusion detection in robotic environments. This contribution is pivotal for identifying and mitigating cybersecurity threats in real-time, offering a scalable solution for securing autonomous systems. With 4 citations in a short time, her work is gaining traction among researchers tackling the intersection of AI and security. Aljohani’s research not only advances theoretical frameworks but also provides practical tools for protecting critical infrastructure, making her a rising voice in the field of cyber-physical systems security.
Research Focus
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Top Papers
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