Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Network intrusion detection system: A systematic study of machine learning and deep learning approaches
2020695 citationsZeeshan Ahmad, Adnan Shahid Khan et al.Transactions on Emerging Telecommunications Technologiesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Johari Abdullah
Since
Specialization
Citations
This map shows the geographic impact of Johari Abdullah's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Johari Abdullah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johari Abdullah more than expected).
This network shows the impact of papers produced by Johari Abdullah. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Johari Abdullah. The network helps show where Johari Abdullah may publish in the future.
Co-authorship network of co-authors of Johari Abdullah
This figure shows the co-authorship network connecting the top 25 collaborators of Johari Abdullah.
A scholar is included among the top collaborators of Johari Abdullah based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Johari Abdullah. Johari Abdullah is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Iskandar, D. N. F. Awang, A. A. Zijlstra, Iain McDonald, et al.. (2020). Classification of Planetary Nebulae through Deep Transfer Learning. Unimas Institutional Repository (Universiti Malaysia Sarawak).2 indexed citations
11.
Ahmad, Zeeshan, Adnan Shahid Khan, Cheah Wai Shiang, Johari Abdullah, & Farhan Ahmad. (2020). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies. 32(1).695 indexed citations breakdown →
Abdullah, Johari, et al.. (2017). Hierarchical Density-based Clustering of Malware Behaviour. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 9. 159–164.5 indexed citations
15.
Ibrahim, Dayang Hanani Abang, et al.. (2017). Achieving Reproducibility Incorporating Service Versioning into Provenance Model. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 9. 131–138.1 indexed citations
16.
Javed, Yasir, et al.. (2017). EEoP: A Lightweight Security Scheme over PKI in D2D Cellular Networks. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 9. 99–105.14 indexed citations
17.
Ibrahim, Dayang Hanani Abang, et al.. (2017). A Methodology for Implementation of Service Learning in Higher Education Institution: A case study from Faculty of Computer Science and Information Technology, UNIMAS. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 9. 101–109.8 indexed citations
18.
Khan, Adnan Shahid, et al.. (2017). Energy Efficient Resource Allocation and Utilization in Future Heterogeneous Cellular Network. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 9. 107–110.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.