Hitham Alhussian

Papers

Total Citations

191

H-Index

About

Hitham Alhussian is a researcher specializing in deep reinforcement learning (DRL) and cybersecurity, with a particular focus on intelligent decision-making systems and the protection of cyber-physical infrastructure. His most influential contribution is a systematic review of the Deep Deterministic Policy Gradient (DDPG) algorithm, which has garnered an impressive 164 citations since its 2024 publication, establishing it as a go-to reference for researchers navigating the rapidly evolving landscape of DRL in high-dimensional action and state spaces. This work, alongside an earlier iteration published in 2023, demonstrates his sustained commitment to synthesizing and advancing knowledge in deep reinforcement learning methodologies. Complementing this, his 2022 research on deep learning models for cybersecurity attack detection in cyber-physical systems addresses critical vulnerabilities in IoT infrastructure, smart manufacturing, and intelligent transportation networks — domains where security breaches carry significant real-world consequences. With a growing citation record and research spanning both theoretical algorithmic analysis and applied security solutions, Alhussian has positioned himself as an emerging voice bridging artificial intelligence and cybersecurity, offering valuable insights for students and professionals working at this increasingly important intersection.