Zeeshan Akram
Papers
1
Total Citations
3
H-Index
1
About
Zeeshan Akram’s research lies at the intersection of cybersecurity and machine learning, with a primary focus on malware detection and network security. His most-cited work, “Role of Logistic Regression in Malware Detection: A Systematic Literature Review” (2022), systematically examines how logistic regression models can identify malicious software, addressing the escalating need for robust security in an era where networks transmit sensitive data across banking, agriculture, robotics, and virtual social platforms. By synthesizing existing approaches, Akram highlights the evolution from early threats like the Brain virus to modern, sophisticated attacks, demonstrating how statistical methods can enhance detection accuracy. His contributions are particularly valuable for students and researchers seeking accessible, data-driven solutions to cybersecurity challenges. With 3 citations to date, this review serves as a foundational resource for those exploring machine learning’s role in safeguarding digital infrastructures. Akram’s work underscores the critical importance of adaptive security measures in our interconnected world, making him a notable voice in the ongoing fight against cyber threats.
Research Focus
Key Achievements
Top Papers
- 1