Musaddak Maher Abdul Zahra

Papers

Total Citations

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About

Musaddak Maher Abdul Zahra is a researcher at the forefront of cybersecurity and intelligent systems, with a primary focus on securing critical infrastructure through advanced cryptographic and machine learning techniques. His most cited work, "Hybrid Encryption Method for Health Monitoring Systems Based on Machine Learning" (2022), addresses the vulnerability of transmission lines to disasters and vandalism by proposing a robust, hybrid encryption framework integrated with machine learning. This contribution is pivotal for protecting wireless sensor networks—composed of disparate, spatially distributed devices—that monitor pipelines and other essential assets. By combining encryption with adaptive learning, Abdul Zahra enhances data confidentiality and system resilience against evolving threats. His research has garnered attention, with this paper alone accumulating 9 citations, reflecting its relevance in the growing field of IoT security. Abdul Zahra’s work stands out for its practical application in safeguarding health monitoring systems and industrial infrastructure, offering a scalable solution that balances security with efficiency. His achievements underscore a commitment to bridging theoretical cryptography with real-world challenges, making him a notable voice in the intersection of machine learning and network protection.